F lack of outliers in the data linear regression is

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f. Lack of outliers in the data Linear Regression is extremely sensitive to outliers as It affects the regression line and eventually the forecasted values.
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Regression 1/18/20 Great Learning 11 5. Two main types of Linear regression a. Simple Linear Regression (SLR) In SLR, there is only one predictor (independent) variable X which changes result on different values for response (dependent) variable, Y. Let Y t denote the dependent variable, X1 t is the independent variable for the t th observation. The value of Y at time t, in the sample data is determined by the linear equation: β0, β1 are constants and εs are independent and identically distributed normal random variables with mean 0. β0 and β1 are intercept and slope of the model. Example: Regression equation between hours studied and marks scored Marks scored = 24.16 + 0.7382 * Hours_Studied
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Regression 1/18/20 Great Learning 12 5. Two main types of Linear regression Note: 1) The relationship is not a mathematical model relationship and hence the error term. 2) The linearity condition is defined with respect to the regression coefficients and not with respect to the predictor variables. Thus Y i = β 0 + β 1 log ( X i) + ε i is a linear model but relationship between Y and X is not linear. 3) Regression calculates the most likely outcome based on a trend of one or more known independent variables and the impact of changing one of the independent variables. a. Simple Linear Regression (SLR) - continued
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Regression 1/18/20 Great Learning 13 5. Two main types of Linear regression - continued b. Multiple Linear Regression (MLR) In MLR, there are more than one predictor (independent) variables X1, X2, … which change result on different values for response (dependent) variable, Y. Let Y t denote the dependent variable, X1 t , X2 t , …, Xk t are the independent variable for the t th observation. The value of Y at time t, in the sample data is determined by the linear equation: β 0 , β 1 , … β k are constants and εs are independent and identically distributed normal random variables with mean 0. β 0 is the intercept of the model and β0, β1,β2, ...βk are regression coefficients Example: Regression equation between hours studied, Gender and marks scored in the final examination Marks = 19.71 + 0.72 * Hours_Studied + 10.18 * Gender.
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Regression 1/18/20 Great Learning 14 6. Simple Linear Regression (SLR) Model building Simple linear regression model helps us to understand how the value of a dependent variable under study changes with the values of an independent variable. Examples include: a. An e-commerce company such as Bigbasket, e-Bay, Amazon would like to understand how their revenue varies with the number of customer visits to their portal. b. Banks and other financial institutions would like to understand the impact of unemployment rate on percentage of non-performing assets. c. Original Equipment Manufacturers would like to understand the impact of duration of warranty on their profit.
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Regression 1/18/20 Great Learning 15 6. Simple Linear Regression (SLR) Model building Steps for building a regression model I. Data collection Collect data from various sources for the identified problem. Data collection is a time consuming and
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