Problem Set 6 (1)

Topic: Simple Regression

The table lists fossil fuel production as a percentage of total energy production for selected years. A linear regression model for this data is (A) Draw a scatter plot of the data and a graph of the model on the same axes. y = - 0.33x+95.0 OA. OB. where x represents years after 1960 and y represents the corresponding percentage of oil imports. 100 100 Fossil Fuel Production Production (%) 96 Year 1960 07 -> 1970 1980 91 60 60 Years after 1060 88 Years after 1980 1990 84 OC. OD. 2000 83 100 , 100 0- 04 60 60 Years after 1960 Years after 1960 (B) Interpret the slope of the model. The rate of change of the percentage of oil imports with respect to time is -0.33% per year. (C) Use the model to predict fossil fuel production in 2010. In 2010 fossil fuel production as a percentage of total production will be about 78.5 %. (Round to one decimal place as needed.) (D) Use the model to estimate the year in which fossil fuel production will fall below 70% of total energy production. In the year…

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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…

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The following data relate the sales figures of restaurant, to the number of customers registered that week: Week   Customers   Sales (SR) First 16 330 Second 12  270 Third 18 380 Fourth 14 300   a) Perform a linear regression that relates bar sales to guests (not to time). b) If the forecast is for 20 guests next week, what are the sales expected to be?

Stores commonly offer a cheaper unit price for large quantity purchases. Quantity 1 2 5 10 20 Unit Price $100.00 $80.00 $70.00 $50.00 $40.00 a. Use regression to find a logarithmic equation to model the data. Round the numbers in your equation to 2 decimal places. y = a + bln(z) with You b b. Use your equation to find an appropriate unit price for a customer who purchases 15 items. c. Use your equation to find an appropriate unit price for a customer who purchases 25 items. $

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Sally Sells Sea Shells by the Sea Shore and collects all sales dataNow she is curious to find out what the elasticity of demand is for her shells Assume they are all the same type and quantity She scatter plots the data and finds there is a linear relationship that looks ripe for a regression estimation of the price response function for her shells The slope of her regression line is 61. Currently, her average daily price is 11.74 and she sells 95 quantity at that priceCalculate the point elasticity of demand for her sea shells

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Expedia wants to use regression analysis to build a model for airfare tickets prices in the states: Ticket prices = 30 + B1*Miles + E where Miles is measured in hundreds Coefficients 169.50 5.90 Intercept Miles (in hundreds) Which of the following is true? Standard Error 1.34 0.09 4 t Stat 126.85 61.28 P-value 0.000 0.002 If Miles increases by 1, then we predict ticket price to go up by $5.9. O If ticket price goes up by $1, then we predict Miles to go up by 590 miles. O If ticket price goes up by $100, then we predict Miles to go up by 590 miles. If Miles increases by 100, then we predict ticket price to go up by $5.9.

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不 Complete parts (a) through (c) using the following data. Row 1 Row 2 0 90 2 2 4 5 5 5 6 5 6 86 78 71 91 73 73 80 56 65 (a) Find the equation of the regression line for the given data, letting Row 1 represent the x-values and Row 2 the y-values. Sketch a scatter plot of the data and draw the regression line. Input the values of the slope and intercept for the regression line when Row 1 represents the x-values. (Round to three decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Plot Row 1 on the horizontal axis and Row 2 on the vertical axis. Choose the correct graph below. OA. O B. ○ C. ○ D. Q 100 G 100p G 100 Q G (b) Find the equation of the regression line for the given data, letting Row 2 represent the x-values and Row 1 the y-values. Sketch a scatter plot of the data and draw the regression line. Input the values of the slope and intercept for the regression line when Row 2 represents the x-values. (Round to three decimal places as…

Use Excel's regression function to develop a multiple regression forecast for Pricilla Phashions Indicate the r² of the model, and whether all the coefficients for the regression are significant, then calculate the dress sales for Year-3. show steps on excel

A company sets different prices for a particular DVD system in eight different regions of the country. The accompanying table shows the numbers of units sold and the corresponding prices (in dollars). Sales 420 380 350 400 440 380 450 420 Price 104 195 148 204 96 256 141 109a. Graph these data, and estimate the linear regression of sales on price. b. What effect would you expect a $50 increase in price to have on sales?

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Please make a Data set and regression equation based on Student Debt and inflation with the following variables: Current student debt,interest rates, inflation rates ( will give thumbs up ) thank you so much.

Conduct a regression analysis

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?)

A scatter plot shows data for the cost of a vintage car from a dealership (y in dollars) in the year a years since 1990. The least squares regression line is given by y-25,000 + 500z. Interpret the y intercept of the least squares regression line. Select the correct answer below O The predicted cost of a vintage car from a dealership in the year is 820.000 O The predicted cost of a vintage car from a dealershpin the year 1090 is 85,000. O The predicted cost of a vintage car from a dealershp in the year 1990 is sse. The yintercept should not be interpreted.

5.2 42 The following regression shows the impact of the number of rooms (ROOM) on the monthly rent of an apartment (in thousand Rands) (RENT) from a sample of 42 randomly chosen apartments in Parow: Dependent Variable: RENT Variable C ROOM r-square: 0.7180 Parameter -0.7705 2.1331 SE of regression: 2.3397 Std. Error 0.8233 0.2114 sample size = 42 What will be values of the parameters, variance and standard errors of the parameters, estimated variance of error terms, coefficient of determination and correlation coefficient, if RENT is measured in Rands and there is no change to the unit of measurement of the ROOM variable?

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Solve for the coefficient of correlation

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.

QUESTION 17 I am trying to figure out how to measure an athlete's productivity. So, I have run a linear regression of a NBA player's salary (dependent variable) on a player's statistics including average points, assists, rebounds per game, and turnovers per game (the independent variables). The final model is: Salary = 1,000,000 * Points per game + 50,000 * Assists per game + 20,000 * Rebounds per game - 30,000 * Turnovers per game %3! Last year, Lebron James averaged 25 points per game, 8 assists per game, 8 rebounds per game and 4 turnovers per game. What is Lebron's predicted salary?

The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4​ months:     Jan    Feb    Mar    AprMonth    1    2    3    4Number of Accidents    30    40    70    105 Using the least-squares regression ​method, the trend equation for forecasting is ​(round your responses to two decimal ​places)​: y = ? + ?x

E D Page view A Read aloud Add text V Draw Highlight Answer Question 41 -45 based on the Information below: Based on the following regression results (Y demand, X prce): Model Log-linear by 0.6652 t= 9.69 6.1533 t-68.72 -20654 t = -13.13 597.79 t = 6.83 Where X = 10562 and Y = 4346. 0.9649 C 122.98 0.0013 C- 15.90 3822 ( = 21.29 0.5331 E = 74.11 Log-lin Lin-log Linear 41. What is the elasticity of demand with respedt to price for the Log-linear model? A. 13.7306 B. 1.2956 C. 0.9649 D. 0.8794 42. What is the elasticity of demand with respect to price for the Log-lin model when price is in average? A. 13.7306 B. 1.2956 C. 0.9649 D. 0.8794 What is the elasticity of demand with respect to price for the Lin-log madel when demand is in average? 43. A. 13.7306 B. 1.2956 C. 0.9649 D. 0.8794

I need Explaination based on the answer key answer is D but how. It is econometrics

What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?

18. A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: D. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?

1. Suppose that you have following data: Variable Description CEO salary measured in thousands of $ Firm's sale measure in millions ofS Return on equity in percent Salary sales roe *Return on equity is a measure of financial performance calculated by dividing net income by shareholders' equity. Your estimated regression is given by log (salary) = 4.322 + 0.276 log(sale) + 0.0215roe - 0.0008roe?, R = 282, n = 209. (324) (0.033) (0.0129) (0.00026) a) Is the effect of all independent variables statistically equal to 0? b) Interpret the coefficient on log(sale). c) Interpret the effect of roe on log(salary). • Without more information, your interpretation of the effect of roe on log(salary) should include answers to these sub-question. Should the roe be included in this model? il. Comment on relationship between roe and log(salary): is it U-shaped or inverse U-shaped? What is the turning point? How would you interpret this point? Plot log(salary) vs roe. v. ii. iv. Compute predicted value…