**Understanding Regression Analysis: Sales Contacts and Sales Dollars**A sales manager at an advertising agency is exploring the relationship between the number of contacts a salesperson makes and the sales dollars earned. A regression analysis was conducted, and the results are outlined below.### Regression Analysis Summary:**Coefficients Table:**- **Intercept:** - **Coefficient:** -12.201 - **Standard Error:** 6.560 - **t-Statistic:** -1.860 - **p-value:** 0.100- **Number of Contacts:** - **Coefficient:** 2.195 - **Standard Error:** 0.176 - **t-Statistic:** 12.505 - **p-value:** 0.000**Interpretation:**- The intercept term (-12.201) indicates the expected sales dollars when no contacts are made; however, it's not statistically significant (p = 0.100).- Each additional contact is associated with an increase of approximately 2.195 dollars in sales, which is statistically significant (p = 0.000).### Analysis of Variance (ANOVA):**ANOVA Table:**- **Regression:** - **df (Degrees of Freedom):** 1.00 - **SS (Sum of Squares):** 13,555.42 - **MS (Mean Square):** 13,555.42 - **F-Statistic:** 156.38 - **Significance F:** 0.00- **Residual:** - **df:** 8.00 - **SS:** 693.48 - **MS:** 86.68- **Total:** - **df:** 9.00 - **SS:** 14,248.90**Interpretation:**- The F-Statistic of 156.38 with a significance level of 0.00 indicates that the model is highly significant overall.### Additional Information:- **Mean of X ($ \bar{X} $):** 33.4- **Sum of Squares of X deviation ($ \sum(X - \bar{X})^2 $):** 2814.4### Prediction Interval:The 95% prediction interval for a salesperson making 30 calls can be calculated using the above model statistics, incorporating variability in

Formula : Prediction interval for an individual y for x = xp is as follows : PI = y^ ±…

Answered: A sales manager for an advertising…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Using the accompanying Cost of Living Adjustments data to find the best multiple regression model to predict the salary as a function of the other variables. What would the comparable salary be for a city with the following adjustments: groceries: 4%; housing: 8%; utilities: −3%; transportation: −1%; and health care: 7%? Use a level of significance of 0.05. Using the accompanying Cost of Living Adjustments data to find the best multiple regression model to predict the salary as a function of the other variables. What would the comparable salary be for a city with the following adjustments: groceries: 4%; housing: 8%; utilities: −3%; transportation: −1%; and health care: 7%? Use a level of significance of 0.05. Determine the best multiple linear regression model to predict the salary as a function of the other rates. Select the best answer below and fill in the corresponding answer boxes to complete your choice. (Type integers or decimals rounded to…
See the attached image for the introduction. What are two-sided 95% confidence limits for the increase in mean selling price associated with each additional square foot? (Plugin completely, but you need not simplify.)
In the above regression of air quality on other variables in question 2, R-squared is 0.3829. How do you interpret it?
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Minitab output of this regression is reproduced below. Coefficients Term Constant GDP Price Model Summary S 0.299 Error Total Coef Analysis of Variance Source Regression -0.0861 0.7654 -0.0006 R-sq 0.98% DF • 2 7 9 SE Coef 0.5644 0.0574 0.0028 R-sq(adj) 0.98% Adj SS 33.4163 0.6277 34.0440 T-Value O a. 7 O b. 9 O c. 10 O d. Cannot be determined from the information given. -0.1520 13.3400 -0.1290 Adj MS 16.7082 0.0897 P-Value 0.8837 0.0001 0.8330 F-Value P-Value 186.325 0.049 Referring to the table above, how many observations were there in this study?
Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.9086 A 0.8181 398.0910 Observations ANOVA df SS MS Significance F Regression 2 1.50876E-18 Residual C 7448393.148 F Total 49 42699148.82 Coefficients Standard Error t Stat P-value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household Size 356.2959 33.2009 10.7315 3.12342E-14 a. Complete the missing entries from A to H in this output b. Estimate the annual credit card charges for a three-person household with an annual income of $40,000. C. Did the estimated regression equation provide a good fit to the data? Explain
A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls is
The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?
Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose that someone wants to predict the cost of dinner at a restaurant in a city based on the Zagat food quality ratings. If 10 restaurants in a city are sampled and the regression output is given below, what can we conclude about the slope of food quality? Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0. 2) The slope is 2.521 and therefore differs from 0. 3) The slope significantly differs from 0. 4) Not enough evidence was found to conclude the slope differs significantly from 0. 5) The slope is equal to 0.
Write the formula for the estimated regression line and interpret the slope of the estimated regression line, the intercept of the estimated regression line- Is it meaningful?, and the estimated R2. Based on the fitted regression model, what is the predicted ATST for a child who is 7 years old? What is the correlation between AGE and ATST? Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
Hi, please help with this assignment. Please show the work step by step as I am having trouble understanding how to compute them.
Do a linear regression between ‘Weight’ vs ‘Performance.’ Let ‘Performance’ be the dependent/response variable and ‘Weight’ be the independent/predictor variable.
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Coefficients 1186 213.48 Coefficients - 130.46 294.11 Standard Error Does the sign of the coefficient for the variable safety rating make sense? 123.87 21.89 14.26 356.37 t Stat P-value 9.575 0.0000 9.752 0.0000 -9.149 0.0000 0.825 0.4128

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS