MAT 240 Module Three Assignment Template

QUESTION 10 Answer questions 10 to 16 based on the regression outputs given in Table 1& 2. Table 1 DATA4-1: Data on single family homes in University City community of San Diego, in 1990. price - sale price in thousands of dollars (Range 199. 9 505) sqft - square feet of living area (Range 1065 - 3000) Table 2 Model 1: OLS, using observations 1-14 Dependent variable: price coefficient std. error t-ratio p-value 52. 3509 0.138750 37. 2855 0.0187329 0. 1857 8. 20e-06 *** const sqft 7. 407 Me dependent var Sun squared resid R-squared F(1, 12) Log-likelihood Schwarz criterion 317. 4929 18273. 57 0. 820522 54. 86051 -70. 08421 145. 4465 Hannan-Quinn S.D. dependent var S.E. of regression Adjusted R-squared P-value (F) Akaike criterion 88. 49816 39. 02304 0. 805565 8. 20e-06 144. 1684 144. 0501 There are observations included in this dataset. It is a. data. O 12; cross-sectional 13; time-series data 14; cross-sectional In this regression model, sale price of a single-family house is the. the…

A manufacturer of computer workstations gathered average monthly sales figures from its 56 dealerships across the country and estimated the demand for its product using the following regression equation: Q=B0+ B₁ PRICE + ẞ2 ADV + ẞ3 PRICE C where Q is the number of computer workstations sold monthly, PRICE is the price of the computer workstation, ADV is the advertising expenditures, and PRICE, is the average price of a leading competitor's computer workstation. The regression results are as follows: DEPENDENT VARIABLE: Q PROB(F-STATISTIC) OBSERVATIONS: 56 R-SQUARED 0.68 F-STATISTIC 21.25 Standard 0.04 Variable Coefficient error Intercept 15,000.0 5234.0 PRICE -2.8 1.29 ADV 150.0 175.0 PRICEC 0.2 0.13 a. Which coefficients have a statistically significant effect on the number of workstations sold? Substantiate your answers appropriately. b. Calculate the expected number of workstations sold when PRICE = $7,000, ADV = $52, and PRICE $8,000. c. Calculate the own-price elasticity of…

Water is being poured into a large, cone-shaped cistern. The volume of water, measured in cm³, is reported at different time intervals, measured in seconds. A regression analysis was completed and is displayed in the computer output. Regression Analysis: cuberoot (Volume) versus Time Predictor Coef SE Coef Constant -0.006 0.00017 -35.294 0.000 Time 0.640 0.000018 35512.6 0.000 s=0.030 R-Sq=1.000 R-sq (adj)=1.000 What is the equation of the least-squares regression line? Volume = 0.640 - 0.006(Time) Volume = 0.640 - 0.006(Time) Volume = -0.006 + 0.640(Time) Volume = - 0.006 + 0.640(Time?)

Conduct a regression analysis in Excel using the following data: X Y 12 40 23 50 40 59 33 58 18 45 a) What is the value of b0?  Include 1 decimal place in your answer. b) What is the value of b1?  Include 2 decimal places in your answer.

A finance manager employed by an automobile dealership believes that the number of cars sold in his local market can be predicted by the interest rate charged for a loan. Interest Rate (%) Number of Cars Sold (100s) 3 10 5 7 6 5 8 2   The finance manager performed a regression analysis of the number of cars sold and interest rates using the sample of data above. Shown below is a portion of the regression output. Regression Statistics Multiple R 0.998868 R2 0.997738     Coefficient Intercept 14.88462 Interest Rate -1.61538   1. Are there factors other than interest rate charged for a loan that the finance manager should consider in predicting future car sales? 2. Is interest rate charged for a loan the most important factor to be considered in predicting future car sales? Explain reasoning.The dealership's vice-president of marketing has requested a sales forecast at the prevailing interest rate of 7%. 3. As…

A finance manager employed by an automobile dealership believes that the number of cars sold in his local market can be predicted by the interest rate charged for a loan. Interest Rate (%) Number of Cars Sold (100s) 3 10 5 7 6 5 8 2   The finance manager performed a regression analysis of the number of cars sold and interest rates using the sample of data above. Shown below is a portion of the regression output. Regression Statistics Multiple R 0.998868 R2 0.997738     Coefficient Intercept 14.88462 Interest Rate -1.61538   2. Is interest rate charged for a loan the most important factor to be considered in predicting future car sales? Explain reasoning.The dealership's vice-president of marketing has requested a sales forecast at the prevailing interest rate of 7%. 3. As finance manager, what reasons would you convey to the vice-president in recommending this forecasting model? 4. Is the prediction of car sales…

A finance manager employed by an automobile dealership believes that the number of cars sold in his local market can be predicted by the interest rate charged for a loan. Interest Rate (%) Number of Cars Sold (100s) 3 5 10 7 8 2 The finance manager performed a regression analysis of the number of cars sold and interest rates using the sample of data above. Shown below is a portion of the regression output. Regression Statistics Multiple R0.998868 R2 0.997738 Coefficient |14.88462 Interest Rate -1.61538 Intercept 1. Are there factors other than interest rate charged for a loan that the finance manager should consider in predicting future car sales? 2. Is interest rate charged for a loan the most important factor to be considered in predicting future car sales? Explain your reasoning.The dealership's vice- president of marketing has requested a sales forecast at the prevailing interest rate of 7%. 3. As finance manager, what reasons would you convey to the vice-president in recommending…

Suppose the Sherwin-Williams Company is interested in developing a simple regression model with paint sales (Y) as the dependent variable and selling price (P) as the independent variable. Complete the following worksheet and then use it to determine the estimated regression line. Sales Region Selling Price ($/Gallon) Sales (x 1000 Gal) i 2 Zi Yi Zith 1 15 160 2,400 225 25,600 2 13.5 220 2,970 182.25 48,400 3 16.5 140 2,310 272.25 19,600 4 14.5 190 2,755 210.25 36,100 5 17 140 2,380 289 19,600 6 16 160 2,560 256 25,600 7 13 200 2,600 169 40,000 8 18 150 2,700 324 22,500 9 12 220 2,640 144 48,400 10 15.5 190 2,945 240.25 36,100 Total 151 1,770 2,312 Regression Parameters Estimations Slope (B) Intercept (a) In words, for a dollar increase in the selling price, the expected sales will What is the standard error of the estimate (&)? O 14.889 12.180 13.342 gallons in a given sales region. What is the estimate of the standard deviation of the estimated slope (86)? O 2.636 2.157 2.362 Can you…

Regression analysis was applied between $ sales (y) and $ advertising (r) across all the branches of a major international corporation. The following regression function was obtained. ŷ = 5000 + 7.25r (a) Predict the amount for sales where the advertising amount is $ 1,000,000.00. (b) If the advertising budgets of two branches of the corporation differ by $30,000, then what will be the predicted difference in their sales?

Numerical Answer Only Type Question Enter the numerical value only for the correct answer in the blank box. If a decimal point appears, round it to two decimal places. Assume that the number of visits by a particular customer to a mall located in downtown Toronto is related to the distance from the customer's home. The following regression analysis shows the relationship between the number of times a customer visits(Y)per month and the distance(X, measured in km) from the customer's home to the mall. \[ Y=15-0.5 X \] A customer who lives30 kmaway from the mall will visi______ who lives10 km away. less times than a customer

An economist believes that price, x, (in dollars) is the biggest factor affecting quantity sold, y. To support his argument, he collected data on price and quantity sold from a sample of 29 stores, selling the same product, and generated the regression output in Excel. The regression equation is reported as y = and the correlation coefficient r = - 0.333. 9.45x + 20.86 What proportion of the variation in quantity sold y can be explained by the variation in price? R² = % Report answer as a percentage accurate to one decimal place.

SoCal Edison reported the following data for operating revenue and net income for 2001 through 2005. Year Operating Revenue (Millions), X Net Income (Millions), Y 2001 2270 96.9 2002 1482 89.1 2003 2138 103.9 2004 2260 81.6 2005 2600 78.1   Determine the least-squares regression line and interpret its slope. Estimate the net income if the operating revenue figure is $2500 million.

The regression equation to predict sales based on temperature is:  Predicted sales = -2419.01+ 98.02 (temperature).    A correct interpretation of the slope would be that 1. as temperature goes up by 1 degree, sales are predicted to go down by 2419.01. 2. as temperature goes down by 1 degree, sales are predicted to go up by 2419.01. 3. as temperature goes up by 1 degree, sales are predicted to go down by 98.02. 4. as temperature goes up by 1 degree, sales are predicted to go up by 98.02. 5. None of the answer choices provides a correct interpretation of the slope.

This regression is based on cross-section data of 1744 individuals and the relationship between their weekly earnings (in dollars) and age (in years) during 2020. The regression yielded the following result: Estimated(EARN) = 239.16 + 5.20(Age) , R = 0.05, SER = 287.21 Standard errors are reported as hereunder: SE(intercept) = (20.24) SE(Age) = (0.57) (a) Is the relationship between "Age" and "EARN" statistically significant? (b) Explain the meaning of heterskedasticity. Is there any reason to be concerned about heteroskedasticity in this model? Briefly explain your reasons. (c) Construct a 95% confidence interval for the slope coefficient, and use it to test for the statistical significance of the slope coefficient. (d) Construct a 95% confidence interval for the intercept coefficient, and use it to test for the statistical significance of the intercept coefficient.

XYZ company is interested in quantifying the impact of consumer promotions on the sales of its packaged food product. XYZ has historical data on the following variables for 38 weeks: • Sales: Weekly sales volume in thousands of units.• Prom: Weekly spending on consumer promotions in thousands of Dollars" "A regression analysis was applied to XYZ historical dataset. The dependent variable is weekly Sales and the independent variables are weekly Prom and weekly Lagged Prom (i.e., last week Prom). This is a summary of the regression output:Sales = 0.80 + 1.20*Prom - 0.40*Lag(Prom) • R-squared=0.85• F-Statistic=23.83• p-value=0.001 (for the overall regression)•All regression coefficients are statistically significant at the 5% level." A. What will be the predicted sales volume ? B. What is the gross margin of this net volume impact due to $1000 spending per week on consumer promotions, if brand makes $2.20 gross margin per unit . C. What is the ROI of this promotion? D. What is predicted…

Suppose there are 2 quantitative free variables and 1 variable non free category. Non-free variables have 2 categories, namely 1 for the success category and 1 for the fail category. The method used to create models that describe relationships between variables is a binary logistic regression model. Perform parameter recovery for the model. Explain the stage until the alleged value is obtained

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Consider a data set with 15 observations and consider a multiple linear regression model with 7 in-dependent variables. Assume you have estimated the model and you find that SST = 1,325 and SSR = 794.

The owner of a movie theater company used multiple regression analysis to predict gross revenue  (y)  as a function of television advertising  (x1)  and newspaper advertising  (x2).  The estimated regression equation was ŷ = 83.7 + 2.23x1 + 1.60x2. The computer solution, based on a sample of eight weeks, provided SST = 25.4 and SSR = 23.445. (a)Compute and interpret R2 and Ra2.(Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (??) . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is  (??).

As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…

12 Sum of squares total (SST) is, a 2558.436     b 2610.649     c 2663.927     d 2718.293

Mita, the manufacturer of copiers, has been spending increasing amounts of money on radio and television advertising in recent years. An analyst employed by Mita wanted to estimate a simple linear regression of the company's annual copier sales versus advertising dollars. Th regression results included SSE = 12593 and SSR = 87663. What is the coefficient of determination for this regression? 0.874 0.935 0.144 0.126

A marketing analyst wants to examine the relationship between sales (in $1,000s) and advertising (in $100s) for firms in the food and beverage industry and collects monthly data for 25 firms. He estimates the modet: Sales- Bo + B1 Advertising +t. The following table shows a portion of the regression results. Coefficients Standard Error t-stat p-value Intercept 40.10 14.08 2.848 0.0052 Advertising 2.88 1.52 -1.895 0.0608 Which of the following are the competing hypotheses used to test whether the slope coefficient differs from 3? Multiple Choice Ho i bị 3; HAtbi3 Họ ib - 2.88; HAibi 2.88

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A website that rents movies online recorded the age and the number of movies rented during the past month for some of their customers. The data are shown below for a random sample of 25 of their customers.The regression line for the data, with number of movie rentals as the response variable, provides an intercept = 18.87, and slope = -0.228. The standard error of the slope SE(b1) = 0.0827. Margin of error ME for a 99% Confidence Interval for the slope of the Population regression line is: 0.1161 0.2322 0.4644 0.3483

Refer to the following computer output from estimating the parameters of the nonlinear model Y=aRbsc7d The computer output from the regression analysis is: DEPENDENT VARIABLE: LNY R-SQUARE 32 0.7766 OBSERVATIONS: VARIABLE INTERCEPT LNR P-VALUE ON F 0.0001 PARAMETER ESTIMATE STANDARD ERROR T-RATIO -0.6931 F-RATIO 4.66 -0.44 8.28 32.44 0.32 1.36 -2.17 3.43 -1.83 P-VALUE 1.80 0.0390 LNS 0.24 LNT 4.60 Based on the information in the table, the nonlinear relation can be transformed into the following linear regression model: Multiple Choice in Y= 1n a.ln R.1n S.1n T in Y= 1na + b1nR+ cins + din T 1n Y = 1n(aRb SC7d) Y = 1n(aRb Sc7d) 0.0019 0.0774 0.0826

An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41

(10+05)The following data were collected on the height (inches) and weight (pounds) of women swimmers.Height6870646566 Weight132110106115128 a. Develop the estimated regression equation by computing the values of b0 and b1.