We enter 3 for 3,500 in the equation for prediction.Īt X = 3, = 3 + 5 = 8 or 8 thousand tires sold. Transcribed image text : (a) Make an Excel worksheet to calculate SSxx SSyy, and SSxy. (Round your answers to 3 decimal places.) b bo (c) Use your estimated slope and intercept to make a worksheet to calculate SSE, SSR, and SST.
Overall, Notice that the degrees of freedom add just the way the sums of squares do. SSyy SS.xy (b) Use the formulas to calculate the slope and intercept. SST Total sum of squares SSRRegression (explained) sum of squares SSE Errors (residual) sum of squares. Standard Deviation of Errors (Cont) SSR, which is the difference between SST and SSE, has the remaining one degree of freedom. Since advertising is measured in thousand dollars, Where X41 for female executive 0 for male executive The numbers in the parenthesis are the standard errors. Calculate SSE and MSE, and standard error and t-score of the slope coefficient and comment on the significance of the slope.
Check whether there is a relation between correlation coefficient and coefficient of determination. m is also known as regression co-efficient.It tells whether there is a positive correlation between the dependent and independent variables. Y Predicted Y value for the given X value. Calculate the correlation coefficient and coefficient of determination. Linear Regression equationImage by Author c y-intercept What is the value of y when x is zero The regression line cuts the y-axis at the y-intercept. Also predict the amount of tires (in thousand tires) sold when money invested in advertising is $3500. (X is advertising expenditure in thousand dollars and Y is tires sold in hundreds): ∑X = 50 ∑Y = 100 ∑X2 = 225 ∑Y2 = 720 ∑XY = 390įind the Intercept and slope and Write the Regression Equation. Based on the data set with 20 observations, the simple linear regression model yielded the following results. He randomly selects 6 months of data consisting of tire sales (in hundreds of tires) and advertising expenditures (in thousands of dollars). Because R-squared increases with added predictor variables in the regression model. Note that p includes the intercept, so for example, p is 2 for a linear fit. He believes that the number of tires sold is a linear function of the amount of money invested in advertising. SSE is the sum of squared error, SSR is the sum of squared regression, SST is the sum of squared total, n is the number of observations, and p is the number of regression coefficients. A local tire dealer wants to predict the number of tires sold each monthĪ local tire dealer wants to predict the number of tires sold each month.
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