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In the equation for a line, Y = the vertical value. See examples of simple and multiple linear regression, assumptions, applications, and tools. They might fit a multiple linear regression model using Mar 4, 2024 · Regression analysis, a powerful tool for data analysis, helps businesses and researchers make informed decisions by predicting outcomes based on historical data. We will illustrate the basics of simple and multiple regression and demonstrate Mar 2, 2022 · For example, the time required to run this first basic model was about 30 seconds, which isn’t too bad, but as I’ll demonstrate shortly, this time requirement can increase quickly. " Again, as in the case of simple regression, Proportion Explained = SSY′/SSY (14. Enter your data. 2. Choose the number N tree of trees you want to build and repeat steps 1 and 2. The figure also shows the fitted linear function with beta_0 = -90. Inpatient hospitalization is a source of stress for many patients (like Ms A); hence, a variety of regressive behavior is displayed by patients in general hospital settings. c = constant and a is the slope of the line. Specify and assess your regression model. Aug 12, 2019 · Linear regression is a very simple method but has proven to be very useful for a large number of situations. Regression Analysis Tutorial and Examples. 26721 × age. This exercise is intended to review the concept of piecewise linear regression. 53308 / 2 = 273. The output varies linearly based upon the input. X-axis and the dependent (output) variable i. Jun 17, 2020 · Regression model: Definition, Types, and examples. Let’s say our function looks like this. The p-value for the model can be found by comparing the model to a null model. This can be seen as the scattering of the observed data points about the regression line. Age. Distance. 1335 / 9 = 53. y is the output we want. It may be involuntary, in which case . Contrast this with a classification problem, where the aim is to select a class from a list of classes 1 – r 2 r 2, when expressed as a percentage, represents the percent of variation in y that is NOT explained by variation in x using the regression line. Linear regression has a predictor variable and a dependent variable which is related to each linearly. Make sure that you can load them before trying to run the examples on this page. Keras is a deep learning library that wraps the efficient numerical libraries Theano and TensorFlow. read_csv(‘ 1. But when data is following a nonlinear trend, we need to develop nonlinear regression models. Introduction. In this article, you will learn everything you need to know about Ridge Regression, and how you can start using it in your own machine learning projects. You can use this formula to predict Y, when only X values are known. Here is a summary of the similarities and differences between correlation and regression: Similarities: Robust Poisson regression example . 01165 + 0. May 18, 2020 · I used the house prices dataset as an example, going through each step from data analysis to the machine learning model. Step 4: Check for homoscedasticity. Example #1 Scared of his extremely strict math teacher, Nathan, an high school student, starts bedwetting again. Regression example, part 2: fitting a simple model Having already performed some descriptive data analysis in which we learned quite a bit about relationships and time patterns among the beer price and beer sales variables, let’s naively proceed to fit a simple regression model to predict sales of 18-packs from price of 18-packs . Independent Variables: These are the factors you think may affect the dependent variable. Where: Y is a vector containing all the values from the dependent variables. May 12, 2020 · Multiple Linear Regression: It’s a form of linear regression that is used when there are two or more predictors. Doctors and mental health professionals may conduct lab tests and various screenings to diagnose regression, and potential Example. The interpretation of the slope is that the average FEV increases 0. Examples of regressive behaviors include baby talk, thumb-sucking, and temper tantrums. The correlation coefficient is r = . Figure 1 – Weighted regression data + OLS regression. Jan 28, 2023 · In civil engineering, nonlinear regression is used to model relationships between components of cement (such as limestone, sand, clay, additives, etc. regressor. If there is a single input variable X Oct 27, 2020 · The Logistic Regression Equation. When implementing simple linear regression, you typically start with a given set of input-output (𝑥-𝑦) pairs. This means that 83. We will see how multiple input variables together influence the output variable, while also learning how the calculations differ from that of Simple LR model. Dec 15, 2023 · The following are the formula and examples: Simple Linear Regression: Formula of simple linear regression: y = β0 + βx. It models the relationship between the input features and the target variable, allowing for the estimation or prediction of numerical values. " The pieces can be connected or not connected. Figure 2 shows the WLS (weighted least squares) regression output. 046. The estimated regression equation is that average FEV = 0. Shipment data. I gave an example of feature engineering extracting a feature from raw data. Many behavioral and motoric symptoms may cause catatonia May 9, 2024 · Learn how to use linear regression to model and predict the relationships between variables. fit(X_train, y_train) # fit the model. Regression is considered a type of defense mechanism, uniquely characterized by age-inappropriate behaviours and impulses that typically manifest as a way of communicating distress. This first chapter will cover topics in simple and multiple regression, as well as the supporting tasks that are important in preparing to analyze your data, e. Computations are shown below. The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear Mar 28, 2024 · Regression to the mean is the statistical tendency for an extreme sample or observed value to be followed by a more average one. Example's of the discrete output is predicting whether a patient has cancer or not, predicting whether the customer will churn. where: Xj: The jth predictor variable. In this example, the observed values fall an average of 5. So, if the slope is 3, then as X increases by 1, Y increases by 1 X 3 = 3. X = the horizontal value. Step 3: Perform the linear regression analysis. Jan 6, 2024 · Local regression is a statistical method used for estimating the relationship between variables in a data set by adjusting to the local features of the data by utilizing the weighted average of surrounding points. Linear Regression Analysis. 7% of the variation in exam scores can be explained by the number of hours studied. The purpose of polynomial regression is to capture a wider range of curvature in the data. 26721 × (8) = 2. 6631 2 = . Note: For larger datasets (n_samples >= 10000), please refer to Equation for a Line. After developers fix this issue, the login button is checked to make sure it works as it should. The idea behind simple linear regression is to "fit" the observations of two variables into a linear relationship between them. For example, suppose we have the following dataset with the weight and height of seven individuals: Robust regression methods provide an alternative to least squares regression by requiring less restrictive assumptions. The line of best fit is: ŷ = –173. The aim is to establish a mathematical formula between the the response variable (Y) and the predictor variables (Xs). The response variable is a continuous numeric variable. Use when: The relationship between the predictor variable (s) and the response variable is non-linear. Regression is the task of predicting a continuous quantity. This type of regression analysis is one of the most basic types of regression and is used extensively in machine learning. Regression may be seen at any stage of development in both adults and children when someone behaves in a way that's immature or inappropriate for their age. regressor = LinearRegression() # Instatiate LinearREgression object. The anova function can be used to conduct an analysis of deviance. Jun 7, 2024 · Regression is a psychological defense mechanism in which an individual copes with stressful or anxiety-provoking relationships or situations by retreating to an earlier developmental stage. Linear regression is estimated using Ordinary Least Squares (OLS) while logistic regression is estimated using Maximum Likelihood Estimation (MLE May 9, 2020 · Regression analysis is primarily used for two distinct purposes. If the dependent variable is dichotomous, then logistic regression should be used. Graphically, the task is to draw the line that is "best-fitting" or "closest" to the points \ ( (x_i,y_i),\) where \ ( x_i Oct 5, 2023 · In a previous lesson, we have discussed linear regression models. B = the value of Y when X = 0 (i. Oct 12, 2023 · In this article, we are able to explore the basics of regression metrics in scikit-learn, discuss the steps needed to use them effectively, provide some examples, and show the desired output for each metric. A common case study is the ability to model a slump test result for cement using just data, and comparing these results to the real world slump test. X is a matrix where each column is all of the values for a given independent variable. 837. Jun 14, 2024 · It sometimes feels like a hectic task for most beginners, so let’s crack that out and understand how to perform Polynomial Regression in Machine Learning in 3-d space. x is the input variable. Slope — Angle of the line / denoted as m or 𝛽1. Start by entering or uploading your data into a statistical program like R, Stata, Excel, or Desmos. 8) In simple regression, the proportion of variance explained is equal to r2; in multiple regression, the proportion of variance explained is equal to R2. From finance to healthcare and market research, it serves as a cornerstone to predict future trends and mitigate risk in business decisions. We can use our model’s . 6631. The regression model in data analysis is a powerful statistical analysis tool that helps unlock relevant insights from data and make the right decision. An example of the continuous output is house price and stock price. Mar 25, 2024 · Once you have your data in a table, enter the regression model you want to try. In statistics, linear regression is a statistical model which estimates the linear relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables ). The above equation is referred to as a VAR (1) model, because, each equation is of order 1, that is, it contains up to one lag of each of Jun 13, 2024 · Linear regression is a quiet and the simplest statistical regression technique used for predictive analysis in machine learning. Moreover, it is based on the concept of smoothing, which includes decreasing the variability or noise in the data. The 2019 research paper “A Spatial Regression Analysis on Neighborhood-Level Trust and Cooperative Behaviors” explores the use of spatial regression to analyze social influence. These methods attempt to dampen the influence of outlying cases in order to provide a better fit to the majority of the data. The least squares regression line (best-fit line) for the third-exam/final-exam example has the equation: y ^ = − 173. Regression. In scikit Apr 26, 2021 · For example, if a multioutput regression problem required the prediction of three values y1, y2 and y3 given an input X, then this could be partitioned into three single-output regression problems: Problem 1: Given X, predict y1. Jul 7, 2023 · The regression example below models the relationship between body mass index (BMI) and body fat percent. Table of contents. It is also known as reverting to the mean, highlighting the propensity for a later observation to move closer to the mean after an extreme value. e. the approach with multiple columns is the same. The other variable, y, is known as the response variable. predict(x_test) Now the model’s predictions are stored in the variable predictions, which is a Numpy array. 68151. uptonpark/iStock/Getty Images. Outliers have a tendency to pull the least squares fit too far in their direction by receiving much Y = Xβ + e. One variable, x, is known as the predictor variable. Correlation vs. Hence, these are a few key differences between correlation and Feb 1, 2021 · In this example, r 2 = 0. After completing this step-by-step tutorial, you will This can be seen as the scattering of the observed data points about the regression line. 4% of the variation in the exam scores can be explained by the number of hours studied and the number of prep exams taken. Simple linear regression. 1. Aug 8, 2023 · The regression coefficient, β 1 \beta_1 β 1 , is the slope of the regression line. May 19, 2020 · Linear Regression Real Life Example #3. M = slope (rise/run). Conversely, if the slope is -3, then Regression (psychology) In psychoanalytic theory, regression is a defense mechanism involving the reversion of the ego to an earlier stage of psychosexual development, as a reaction to an overwhelming external problem or internal conflict. Mar 31, 2021 · Logistic Function (Image by author) Hence the name logistic regression. Here are a few that you can go through. Example #1. The regression equation is a linear equation of the form: ŷ = b 0 + b 1 x . Dec 16, 2023 · Polynomial Regression, a more complex form of regression analysis, extends the linear model by adding extra predictors, obtained by raising each of the original predictors to a power. Let us take the help of a few examples to understand the topic. 4397. Apr 10, 2021 · An example of linear regression can be seen in the figure 4 above where P=5. 83 x. βj: The coefficient estimate for the jth predictor variable. Suppose you have data on income—measured in thousands of dollars per year—and life expectancy—measured in years. Mar 25, 2024 · Logistic Regression: Logistic regression is used when the dependent variable is binary or categorical. 15. Amy Gallo. *. csv’) After running it, the data from the . 83x. org/math/statistics-probability/describ Linear regression is a technique used to model the relationships between observed variables. Here, we will train a model to tackle a diabetes regression task. For a linear model, use y1 y 1 ~ mx1 +b m x 1 + b or for a quadratic model, try y1 y 1 ~ ax2 1+bx1 +c a x 1 2 + b x 1 + c and so on. Apr 23, 2022 · The sum of squares predicted is also referred to as the "sum of squares explained. Jun 8, 2016 · Regression Tutorial with the Keras Deep Learning Library in Python. Dependent Variable: This is what you’re trying to predict or explain. More specifically, Regression analysis helps us to understand how the value of the dependent variable is changing corresponding to an independent variable when other Python has methods for finding a relationship between data-points and to draw a line of linear regression. Regression analysis problem works with if output Gradient boosting can be used for regression and classification problems. e is a vector of residuals. It specifically helps determine how much a dependent variable (Y) is affected by one or more independent variables (X), where: Y – is the dependent variable. In this example, regression MS = 546. Build the decision tree associated to these K data points. After watching this full lecture about Regression, you will know what regression analysis is and what the difference between simple and multiple linear regre Jan 8, 2024 · Examples. We will obtain the results from GradientBoostingRegressor with least squares loss and 500 regression trees of depth 4. There are two main approaches to implementing this Learn how to use regression analysis to estimate and model the relationships between variables in finance and other disciplines. Oct 27, 2020 · In this example, 73. In this post, you will discover exactly how linear regression works step-by-step. , data checking, getting familiar with your data file, and examining the distribution of your variables. Y-axis, called linear regression. Sep 8, 2022 · Fitting the simple linear regression to the Training Set. Correlation helps to constitute the connection between the two variables. A linear regression model is used when the response variable takes on a continuous value such as: Price. In this case, “number of traffic accidents” is the response variable Nov 28, 2022 · Simple linear regression is a statistical method you can use to understand the relationship between two variables, x and y. Now that we did our basic random forest regression, we will look to find a better performing choice of parameters and will do this utilizing the GridSearchCV Aug 7, 2021 · Here’s a summary of the differences: Difference #1: Type of Response Variable. X – is the independent (explanatory) variable. The basic idea behind piecewise linear regression is that if the data follow different linear trends over different regions of the data then we should model the regression function in "pieces. predictions = regressor. For instance, for an 8 year old we can use the equation to estimate that the average FEV = 0. Agricultural scientists often use linear regression to measure the effect of fertilizer and water on crop yields. For example, scientists might use different amounts of fertilizer and water on different fields and see how it affects crop yield. In our student grade example, the final grade would be the dependent variable. Robust regression is an alternative to least squares regression when data are contaminated with outliers or influential observations, and it can also be used for the purpose of detecting influential observations. We can write the following code: data = pd. When the independent variables are highly correlated to each other then the variables are said to be multicollinear. Problem 2: Given X, predict y2. There are several regression examples that one can observe on a day-to-day basis. How to perform all of the calculations using a spreadsheet. Regression helps in estimating a variable’s value based on another given value. y is the output which is determined by input x. There is some overlap between the algorithms for classification and regression; for example: A classification algorithm may predict a continuous value, but the continuous value is in the form of a probability for a class Nov 5, 2010 · Courses on Khan Academy are always 100% free. To test the regressor, we need to use it to predict on our test data. 915 2 = 0. Where: X – the value of the independent variable, Y – the value of the dependent variable. ) to its strength. where x is the number of bedrooms in the house. November 04, 2015. 01. The right side of the figure shows the usual OLS regression, where the weights in column C are not taken into account. See examples of real data sets, Minitab output, and statistical concepts such as R 2, r, and correlation coefficient. It is because it causes problems in ranking variables based on its importance. See examples, formulas, methods, and assumptions for simple and multiple linear regression. Problem 3: Given X, predict y3. You probably know by now that Jan 22, 2024 · 5 Types of Regression Analysis and When to Use Them. This page uses the following packages. Think back to algebra and the equation for a line: y = mx + b. Many types of regression techniques assumes multicollinearity should not be present in the dataset. Here are some examples: Catatonia, a psychomotor syndrome, may be a cause of regression in some individuals. Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. These behaviors can be voluntary or involuntary. A positive regression e. Nov 4, 2015 · A Refresher on Regression Analysis. Linear regression assumes that the relationship between the dependant (y) and independent (x) variables are linear. csv file will be loaded in the data variable. May 14, 2015 · Regression entails retreating to an earlier developmental form of function (emotionally, socially, and behaviorally) in times of stress. Mar 20, 2019 · The regression mean squares is calculated by regression SS / regression df. It provides you with an estimate of how much the dependent variable, Y, will change in response to a 1-unit increase in the dependent variable, X. It is a supervised machine learning technique, used to predict the value of the dependent variable for new, unseen data. Now, let’s load it in a new variable called: data using the pandas method: ‘read_csv’. For example, consider a situation where a tester finds an issue with the login button. Start practicing—and saving your progress—now: https://www. Consider the third exam/final exam example introduced in the previous section THIRD EXAM vs FINAL EXAM EXAMPLE: The graph of the line of best fit for the third-exam/final-exam example is as follows: Figure 12. This logistic function is a simple strategy to map the linear combination “z”, lying in the (-inf,inf) range to the probability interval of [0,1] (in the context of logistic regression, this z will be called the log(odd) or logit or log(p/1-p)) (see the above plot). For a new data point, make each one of your Ntree May 4, 2017 · The general procedure for using regression to make good predictions is the following: Research the subject-area so you can build on the work of others. Example scenario: Predicting house prices based on size. In a regression problem, the aim is to predict the output of a continuous value, like a price or a probability. Nov 28, 2020 · When performing simple linear regression, the four main components are: Dependent Variable — Target variable / will be estimated and predicted. Jul 23, 2021 · 3. Interpret r 2 in the Oct 16, 2021 · Make sure that you save it in the folder of the user. It enhances regular linear regression by slightly changing its cost function, which results in less overfit models. First, it is widely used for prediction and forecasting, which overlaps with the field of machine learning. , y-intercept). Then we say that a predicted point is Yhat = Xβ, and using matrix algebra we get to β = (X'X)^(-1) (X'Y) Comment. Dec 21, 2023 · What is a regression test example? Regression testing is carried out each time a change is made. Feb 26, 2024 · Regression in Machine Learning. by. : f (x) = 60000x f (x) = 60000x. Regression: Similarities & Differences. Stay organized with collections Save and categorize content based on your preferences. Sep 2, 2020 · Step 4: Testing the Linear Regressor. Regression is a defense mechanism in which people seem to return to an earlier developmental stage. Step 1. Aiding in forecasting, risk assessment, and identifying trends, regression analysis plays an important role in diverse fields. The regression coefficient can be any number from − ∞-\infty − ∞ to ∞ \infty ∞. Sep 21, 2020 · Steps to perform the random forest regression. In a different blog post, I use this model to show how to make predictions with regression analysis. The coefficient of determination is r 2 = . Independent Variable — Predictor variable / used to estimate and predict. Apr 17, 2024 · The regression formula in statistics is a method to estimate or calculate the relation between two or more variables. 83 x y ^ = − 173. For example, the leftmost observation has the input 𝑥 = 5 and the actual output, or response, 𝑦 = 5. [1] Apr 30, 2019 · Example 2: Poisson regression can be used to examine the number of traffic accidents at a particular intersection based on weather conditions (“sunny”, “cloudy”, “rainy”) and whether or not a special event is taking place in the city (“yes” or “no”). 4 days ago · Multiple Linear Regression - MLR: Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable. Understanding one of the most important types of data analysis. Linear regression shows the linear relationship between the independent (predictor) variable i. It also empowers decision-makers with data-driven insights. Regression fashions are algorithms used to expect continuous numerical values primarily based on entering features. Logistic regression uses a method known as maximum likelihood estimation (details will not be covered here) to find an equation of the following form: log [p (X) / (1-p (X))] = β0 + β1X1 + β2X2 + … + βpXp. a = slope. Aug 26, 2017 · Linear Regression. Second, it is also used to infer causal relationships between independent and dependent variables. In this example, residual MS = 483. Example of simple linear regression. A simple example of such models is the drag force on a parachute, which is related to the square of the velocity of the parachutist. khanacademy. Consider the third exam/final exam example introduced in the previous section. Please note the ~ is usually to the left of the 1 on a keyboard or in the bottom row of the ABC part of the Desmos keypad. 11. The logistic regression model applies a logistic or sigmoid function to the linear combination of the independent variables. Jun 26, 2021 · In other terms, we plug the number of bedrooms into our linear function and what we receive is the estimated price: f (number\ of\ bedrooms) = price f (number of bedrooms) = price. Step 1: Load the data into R. In the exploratory section, I analyzed the case of a single categorical variable, a single numerical variable, and how they interact together. 8. Dec 5, 2023 · Regression is a normal and temporary condition for children, and it can be a coping mechanism for stress and untreated trauma in adults. This example uses the glmRob function in the robust package. First, we solve for the regression coefficient (b 1): Aug 1, 2023 · Below are steps you can follow to calculate a linear-log model. Feb 25, 2020 · Simple regression dataset Multiple regression dataset. Example 1: Conduct weighted regression for that data in columns A, B, and C of Figure 1. We have seen equation like below in maths classes. I am taking 2 input columns and one output column. We will show you how to use these methods instead of going through the mathematic formula. To conduct a regression analysis, we need to solve for b 0 and b 1. Regression, in psychology, return of the ego to an earlier stage of development. Robust Poisson regression is robust to outliers in the dependent variable. predictmethod to do this. Here you Learn how to use simple linear regression to summarize and study relationships between two continuous variables. 798 and beta_1 = 0. Β0 – is a constant (shows the value of Y when the value of X=0) Β1 – the regression coefficient (shows how much Y changes for each unit change in X) Example 1: You have to study the Mar 12, 2017 · Linear regression is used to predict the value of a continuous variable Y based on one or more input predictor variables X. These pairs are your observations, shown as green circles in the figure. Jun 17, 2024 · Regression can have many causes besides emotional distress such as stress, anxiety, or trauma. Logistic Regression Model: p = 1 / (1 + e^- (β0 + β1X1 + β2X2 + … + βnXn)) In the formula: p represents the Jun 26, 2021 · Ridge Regression is an adaptation of the popular and widely used linear regression algorithm. Step 1: Creating a dataset. In the example below, the x-axis represents age, and the y-axis represents speed. There are psychiatric and medical cases that could potentially lead to regression. Collect data for the relevant variables. At the end, I include examples of different types Mar 26, 2018 · 2. Mar 21, 2024 · An example of Multiple Linear Regression (Bevans, 2023) Key Concepts and Terminology. g. We have registered the age and speed of 13 cars as they were Jul 12, 2024 · Basic regression: Predict fuel efficiency. In this post, you will discover how to develop and evaluate neural network models using Keras for a regression problem. 2665. Examples of Regression. This research helps with the subsequent steps. Polynomial regression is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. In the same example Jul 7, 2019 · For example, the system of equations for a VAR (1) model with two time series (variables `Y1` and `Y2`) is as follows: Where, Y {1,t-1} and Y {2,t-1} are the first lag of time series Y1 and Y2 respectively. We will also build a regression model using Python. Step 2: Make sure your data meet the assumptions. The concept applies only to random variation in a process or system and and the simple linear regression equation is: Y = Β0 + Β1X. Height. This tutorial covers many aspects of regression analysis including: choosing the type of regression analysis to use, specifying the model, interpreting the results, determining how well the model fits, making predictions, and checking the assumptions. 26721 for each one year increase in age (in the observed age range). Getting started in R. Jul 2, 2024 · Regression is a statistical measurement that attempts to determine the strength of the relationship between one dependent variable and a series of other variables. The most popular form of regression is linear regression, which is used to predict the value of one numeric (continuous) response variable based on one or more predictor variables (continuous or categorical). The residual mean squares is calculated by residual SS / residual df. This model is represented as y = a + b ∗ x + c ∗ x 2 + d ∗ x 3 + …. Notice that all of our inputs for the regression analysis come from the above three tables. 51 + 4. In the formula, y is the dependent variable, x is the independent variable, β0 is the intercept and β is the slope. 366 units from the regression line. Polynomial Regression. Most people think the name “linear regression” comes from a straight line relationship between the variables. Instead of administrative boundaries, it introduces a fresh method of defining Classification is the task of predicting a discrete class label. (If the split between the two levels of the dependent variable is close to 50-50, then both logistic and linear regression Regression analysis is a statistical method to model the relationship between a dependent (target) and independent (predictor) variables with one or more independent variables. An age-inappropriate temper tantrum can be one example of regression. Create an object of linear regression and train the model with the training datasets. After reading this post you will know: How to calculate a simple linear regression step-by-step. Standard error: This is the average distance that the observed values fall from the regression line. This tends to occur around periods of stress—for example, an overwhelmed child may revert to However, regression specifies the effect of the change in the unit in the known variable (p) on the evaluated variable (q). Multicollinearity. The goal of Feb 23, 2024 · Regression, often defined as behavior reverting to a prior stage of development, can be a defense mechanism provoked by anxiety or stressful situations. This is a four step process and our steps are as follows: Pick a random K data points from the training set. Conversely, a logistic regression model is used when the response variable takes on a categorical value such as: Yes or No. Step 5: Visualize the results with a graph. y = c + ax c = constant. It is a linear model that uses a quadratic (squared) term to model the curved relationship. af dn gh su br fy gy ef za yu