**Regression Analysis**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 $\overline{X} = 33.4$ and $\sum (X - \overline{X})^2 = 2814.4$. Rounding to one decimal place, the 95% confidence interval for 30 calls is _______.

Answered: akes and the amount of sales dollars…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A regression analysis done with Excel for the relationship between The placement test scores and the final grades for 20 students who took the regular course were recorded and yielded the following output. SUMMARY OUTPUT Regression Storistics Multiple R 0.45351 R Square 0.20567 Adjusted F 0.16155 Standard E 16.1747 Observatic 20 ANOVA MS F SS 4.66073 1219.35 gnificance F 0.04461 1 1219.35 Regressior Residual Total 18 4709.2 261.622 19 5928.55 Chart Area Coefficientsandard Errt Stat P-value Lower 95% Upper 95%ower 95.0%pper 95.0% 0.0192 5.95316 59.0587 5.95316 59.0587 Intercept 32.5059 12.6386 2.57195 X Variable 0.47106 0.2182 2.15887 0.04461 0.01264 0.92948 0.01264 0.92948 According to the model, If the placement test score is 79, the course grade will be A. 32.51 B. 69.64 C. 32.95 D. 91
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 ANOVA shows the following results. 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 What is the value of the coefficient of determination? Multiple Choice −0.9513 0.9754 0.6319 0.9513
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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 0.9086 R Square A Adjusted R Square 0.8181 Standard Error 398.091 Observations B ANOVA df SS MS F Significance F Regression 2 C E G 1.50876E-18 Residual D 7448393 F Total 49 42699149 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 outputb. Estimate the annual credit card charges…
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?
Please help me to interpret all the value.
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A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 2020 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 1 of 2: How many independent variables are included in the regression model

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