Exam 29: Models for Decision Making

The time series graph below shows annual sales figures (in thousands of dollars) For a well known department store chain. Which model would be most appropriate for Forecasting this series?

Data were collected for a sample of 12 pharmacists to determine if years of Experience and salary are related. Below are the regression analysis results. The Dependent variable is Salary in thousands of dollars. The standard error of the slope for This estimated regression equation is Regression Analysis: Salary versus Years Experience The regression equation is Salary $= 37.2 + 1.49$ Years Experience Predictor Coef SE Coef T P Constant 37.164 3.381 Years Experience 1.4882 0.2149 S =5.58485 R-Sq =82.8\%

Weekly commodity prices for heating oil (in cents) were obtained for a period of 30 weeks and regressed against time. Based on the regression output shown below, the Durbin-Watson statistic indicates The regression equation is Price (cents) $=128+1.08$ Time Predictor Coef SE Coef T P Constant 128.112 2.092 61.25 0.000 Time 1.0782 0.1407 7.66 0.000 $\mathrm{S}=5.07299 \quad \mathrm{R}-\mathrm{Sq}=71.9 \%$ Durbin-Watson statistic $=0.244822$

Quarterly returns were forecasted for a mutual fund comprised of technology Stocks. The forecast errors for the last six quarters are as follows: -.47, 1.12, -.85, 1.27, )07, and -.05. The MSE based on these forecast errors is

The residual plot for a linear regression model is shown below. Which of the Following statements is true?

In order to examine if there is a relationship between the size of cash bonuses and Pay scale, data were obtained on the average annual cash bonus and the average annual Pay for a sample of 20 companies. Below is the regression analysis output with annual Cash bonus as the dependent variable. Which of the following statement is true about the Correlation between average annual cash bonus and average annual pay using ? = 0.05? Regression Analysis: Cash Bonus versus Pay The regression equation is Cash Bonus $= - 4877 + 0.245$ Pay Predictor Coef SE Coef T P Constant -4877 9106 -0.54 0.599 Pay 0.2453 0.1079 2.27 0.036 $S = 13188.6 \quad \mathrm { R } - \mathrm { Sq } = 22.3 \%$

Based on returns for the last six months of 2007 for a social choice portfolio Comprised of "green" companies shown below, the forecasted monthly return for January 2008 using a three-month moving average is Month Monthly Return July 2.2\% August 2.5 September 1.8 October 1.4 November 1.1 December 1.9

A least squares estimated regression line has been fitted to a set of data and the Resulting residual plot is shown. Which is true?

The time series graph below shows monthly sales figures for a specialty gift item Sold on the Home Shopping Network (HSN). The dominant component in these data is

Stock prices and earnings per share (EPS) data were collected for a sample of 15 Companies. Below are the regression results. The dependent variable is Stock Price. What is the correlation between stock price and EPS? Regression Analysis: Stock Price versus EPS The regression equation is Stock Price $= - 0.49 + 14.8$ EPS Predictor Coef SE Coef T P Constant -0.486 4.032 -0.12 0.906 EPS 14.8129 0.9437 15.70 0.000 $S = 7.63235 \quad \mathrm { R } - \mathrm { Sq } = 95.0\%$

For many countries tourism is an important source of revenue. Data are collected On the number of foreign visitors to a country (in millions) and total tourism revenue (in Billions of dollars) for a sample of 10 countries. Below is partial regression analysis Output with tourism revenue as the dependent variable. How much of the variability in Tourism revenue is accounted for by the number of foreign visitors? Regression Analysis: Tourism ($ bill) versus Visitors (mill) The regression equation is Tourism $(\$$ bill $)=21.5+0.295$ Visitors (mill) Predictor Coef sE Coef T P Constant 21.464 3.462 Visitors (mil1) 0.29497 0.07917 $S=2.58307 \quad \mathrm{R}-\mathrm{Sq}=63.4\%$

Data were collected for a sample of 12 pharmacists to determine if years of Experience and salary are related. Below are the regression analysis results. The Dependent variable is Salary in thousands of dollars. The calculated t-statistic to test Whether the regression slope is significant is Regression Analysis: Salary versus Years Experience The regression equation is Salary $= 37.2 + 1.49$ Years Experience Predictor Coef SE Coef T P Constant 37.164 3.381 Years Experience 1.4882 0.2149 $S = 5.58485 \quad \mathrm { R } - \mathrm { Sq } = 82.8\%$

For many countries tourism is an important source of revenue. Data are collected On the number of foreign visitors to a country (in millions) and total tourism revenue (in Billions of dollars) for a sample of 10 countries. Below is the regression analysis output With tourism revenue as the dependent variable. The standard error of the slope for this Estimated regression equation is Regression Analysis: Tourism (\$ bill) versus Visitors (mill) The regression equation is Tourism $( \$$ bill $) = 21.5 + 0.295$ visitors $( m i l 1 )$ Predictor Coef SE Coef T P Constant 21.464 3.462 Visitors (mill) 0.29497 0.07917 $S = 2.58307 \quad \mathrm { R } - \mathrm { Sq } = 63.4 \%$

A farmer has increased his wheat production by about the same amount each year. His most useful predictive model is most probably

Stock prices and earnings per share (EPS) data were collected for a sample of 15 Companies. A regression model was fit to these data. From its plots of residuals shown Below, which assumption appears to be violated?

Data were collected for a sample of 12 pharmacists to determine if years of Experience and salary are related. The regression model fit to these data is $\text { Salary } = 37.2 + 1.49 \text { Years Experience }$ Using this regression equation to predict Salary for 10 years of experience gives the following results. Which of the following is True? Fit SE Fit 95\% CI 95\% PI 52.05 1.81 (48.01,56.08) (38.96,65.13)

Based on the actual and forecasted returns for a social choice portfolio shown Below, the MAD is Month Monthly Return Forecast July 2.2\% 1.95\% August 2.5 2.21 September 1.8 2.35 October 1.4 2.15 November 1.1 1.6 December 1.9 1.2

Which statement about re-expressing data is not true? I. Unimodal distributions that are skewed to the left can be made more Symmetric by taking the square root of the variable. II. A curve that is descending as the explanatory variable increases may be Straightened by taking a logarithm of the response variable. III. One goal of re-expression may be to make the variability of the response Variable more uniform.

For many countries tourism is an important source of revenue. Data are collected On the number of foreign visitors to a country (in millions) and total tourism revenue (in Billions of dollars) for a sample of 10 countries. The regression equation fit was $\text { Tourism } ( \$ \text { bill } ) = 21.5 + 0.295 \text { visitors (mil1). }$ If we were interested in Predicting the tourism revenue for a particular country that had 30 million foreign Visitors,

The model predicted $\sqrt { \text { distance } } = 3.30 + 0.235 \times \text { speed }$ can be used to predict the Stopping distance (in feet) for a car traveling at a specific speed (in mph). According to This model, about how much distance will a car going 65 mph need to stop?