A random sample of 9 statistics students produced the following data, Midterm Exam Score, x Final Exam Score, y 18 87 84 75 48 10 22 60 46 60 92 46 69 33 93 45 28 60 where x measures the midterm exam score and y measures the final exam score. What is the estimate of the y = a x + b regression slope coefficient?
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A random sample of 9 statistics students produced the following data,
Midterm Exam Score, x | Final Exam Score, y |
18 | 87 |
84 | 75 |
48 | 10 |
22 | 60 |
46 | 60 |
92 | 46 |
69 | 33 |
93 | 45 |
28 | 60 |
where x measures the midterm exam score and y measures the final exam score. What is the estimate of the y = a x + b regression slope coefficient?
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- A random sample of 9 statistics students produced the following data, Midterm Exam Score, x Final Exam Score, y 81 49 62 23 76 84 26 58 96 33 38 36 10 15 15 83 81 28 where x measures the midterm exam score and y measures the final exam score. What is the estimate of the y = a x + b regression intercept coefficient?A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 1. Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is 8.6 cars. 2. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is 0.234. 3. Do the sample data provide evidence that the model is useful for predicting average monthly sales for employees based on their sales experience using α=0.05? The test statistic is (Type an integer or decimal rounded to two decimal places as…A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is _________ cars. (Type an integer or decimal rounded to one decimal place as needed.)
- A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95% confidence interval around the sample slope and interpret its meaning. The confidence interval is (__________,_________). (Type an integer or decimal rounded to three decimal places as needed.)Consider the multiple regression model shown next between the dependent variable Y and four independent variables X1, X2, X3, and X4, which results in the following function:Ŷ = 33 + 8X1 − 6X2 + 16X3 + 18X4For this model, there were 35 observations; SSR = 1,544 and SSE = 600. Assume a 0.01 significance level.Based on the given information, which of the following conclusions is correct about the statistical significance of the overall model? Multiple Choice Reject the null hypothesis that β3 = 0. Do not reject the null hypothesis that β1 = β2 = β3 = β4 = 0. Reject the null hypothesis that β1 = 0. Reject the null hypothesis that β1 = β2 = β3 = β4 = 0.A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The predicted sales is 8.6 cars. The coefficient of determination is 0.234 Test Statistic is 7.04 P-value=
- A multiple regression analysis between yearly income(y in $1,000s), college grade point average(X1) , age of the individuals (X2), and the gender of the individual (X3); zero representing female and one representing male) was performed on a sample of 10 people, and the following results were obtained. Coefficient Standard Error Constant 4.0928 1.4400 X1 10.0230 1.6512 X2 0.1020 0.1225 X3 -4.4811 1.4400 Analysis of Variance Source of Degrees of Sum of Mean Variance Freedom Squares Square F Regression 360.59 Error 23.91 Write the regression equation for the above. Interpret the meaning of the coefficient of X3. Compute the coefficient…A research department of an American automobile company wants to develop a model topredict gasoline mileage (measured in MPG) of the company’s vehicles by using theirhorsepower and weights (measured in pounds). To do this, it took a random sample of 50vehicles to perform a regression analysis as follows: SUMMARYOUTPUTRegression StatisticsMultiple R 0.865689R Square 0.749417Adjusted RSquare 0.738754Standard Error 4.176602Observations 50ANOVAdf SS MS FRegression a 2451.973702 1225.987 dResidual b 819.8680976 cTotal 49 3271.8418CoefficientsStandardError t StatIntercept 58.15708 2.658248208 21.87797Horsepower -0.11753 0.032643428 -3.60028Weight -0.00687 0.001401173 -4.90349(a) State the multiple regression equation. Interpret the meanings of the coefficients forhorsepower and weight.(b) Test the validity of this multiple regression equation at the significance level of 1%. Showyour reasoning.(c) The research department claims that the weight of the vehicle is negatively linearly related…A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 The predicted sales is 8.6 cars. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is (Type an integer or decimal rounded to three decimal places as needed.)
- a) Calculate the least square regression line for X on Y of the given data?b) Calculate the coefficient of correlation of the given date? Interpret the value of the coefficient?For a sample of 8 employees, a personnel director has collected the following data on ownership of company stock, y, versus years with the firm, x. X 6 12 14 6 9 13 15 9 Y 300 408 560 252 288 650 630 522 (a) Determine the least-squares regression line and interpret its slope(b) For an employee who has been with the firm 10 years, what is the predicted number of shares owned? (c) Is there a statistical significance between years of service and ownership of company stocks? (use α=0.05α=0.05)A sample of n = 120 scores were presented using the Tenacity (authority) scores to the following predictors: age gender, SES, tone of voice, and clothing. Using a two-tailed test at the 0.05 level of significance, a multiple regression analysis was computed: 1. tenacity and age (pvalue = 0.043); 2. tenacity and gender (pvalue = 0.102); 3. tenacity and SES (pvalue = 0.40); 4. tenacity and voice (pvalue = 0.001); 5. tenacity and clothing (value = 0.017); SOrs Using the pvalue, provide your DECISION, whether:Accept Ho or Reject Ho, Accept Ha