E4You have been presented with the following data and asked to fit statistical demand functions: REGION123457810SALES(Y)('000gallons)160220140190130160200150210190ADVERTISINGEXPENSES (A)($'000)150160501909060140110200100SELLINGPRICE (P)($/gallon)15.0013.5016.5014.5017.0016.0013.0018.0012.0015.50Use any multiple regression packages to estimate alinear relationship between the dependent variableand the independent variables.a. Non-linear relationship.i. Estimate a logarithmic form of the demand function.ii. Is the estimated demand function 'good'? Explainyour answer.ii. Compare with the linear form above. Elaborate.DISPOSABLEINCOME (M)($'000)19.017.514.021.015.514.521.518.018.520.0

i. Dependent variable: SALES (Y) (‘000 gallons) Independent variables: ADVERTISING EXPENSES (A)…

Answered: REGION L 2 3 1 = SALES (Y) ('000…

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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The data presented is a multiple logistic regression analysis and the models are shown below. In the models below, the data are coded as follows: p = the proportion of children with a diagnosis of ADHD, Child Exposed and Father’s Diagnosis are coded as 1 = yes and 0 = no. What is the odds ratio adjusted for father’s diagnosis? (Hint: Use only the appropriate model to find the odds ratio) (1) = –2.216 + 1.480 Child exposed (2) Father with diagnosis: = –1.665 + 1.297 Child exposed (3) Father without diagnosis: = –2.343 + 0.823 Child exposed (4) = –2.398 + 1.501 Child exposed + 0.906 Father’s diagnosis A) 4.49 B) 7 C) 2.88 D) 1.501
Describe the patterns in quantity sold and own and rival prices during this time period using basic descriptive statistics. Graphs are welcome as well. Take the logs of the variables, and estimate the demand function. Interpret the R-square. Interpret the coefficients for logP and logPsub Interpret the p-values associated with each independent variable Are consumers price sensitive? Why or why not? (be as precise as you can – you have estimates!). Does this price sensitivity make sense given the good we are examining? How sensitive are our consumers to changes in the rival good’s price? Explain in detail. Suppose we decide to charge a per ounce price of $2, while at the same time our rival charges a price of $2.15. All else equal, what would you expect sales to be? How confident are you in your forecast? Suppose we are charging a price of $2 and our current marginal cost is $1.50 Are we maximizing profits at this price? If not, should we raise or lower price? Why?
Your Question: A psychologist believed that as children get older, they engage in more “inactive activities” such as playing computer games, watching TV and surfing the internet. She collected data from 10 children. She recorded the age of each child and the amount of activity (in hours) the child engaged in per week. The data is below: Age Activity 7 4.3 9 3.2 10 1 13 1.5 12 1.2 15 0.8 9 3 12 4 15 1.5 14 1.9 Using excel, obtain a scatterplot of the data. Discuss the relationship between age and activity based on the scatterplot. Using excel, obtain the simple linear regression linear regression line predicting activity from age. Interpret the intercept and slope of the regression line. By hand, predict the amount of activity for age = 14. By hand, calculate the residual for age = 14. Using excel, test the null hypothesis that the slope coefficient is zero using the p-value approach. Write one sentence interpreting the meaning of this result. Make sure to…
The following table contains the demand from the last 10 months: MONTH ACTUAL DEMAND 1 31 2 34 3 33 4 35 5 37 6 36 7 38 8 40 9 40 10 41 a. Calculate the single exponential smoothing forecast for these data using an α of 0.30 and an initial forecast ( F1�1 ) of 31. Note: Round your intermediate calculations and answers to 2 decimal places. b. Calculate the exponential smoothing with trend forecast for these data using an α of 0.30, a δ of 0.30, an initial trend forecast ( T1�1 ) of 1, and an initial exponentially smoothed forecast ( F1�1 ) of 30. Note: Round your intermediate calculations and answers to 2 decimal places. c-1. Calculate the mean absolute deviation (MAD) for the last nine months of forecasts. Note: Round your intermediate calculations and answers to 2 decimal places. c-2. Which is best? multiple choice Exponential smoothing with trend forecast Single exponential smoothing forecast
Obtain the regression of demand on price from the following data and estimat demand when the price is 7.5 Price Demand Price Demand 2.0 75 4.5 40 2.5 60 5.0 40 3.0 55 5.5 35 3.5 50 6.0 30 4.0 45 6.5 30
please answer these two correctly;
As a sales analyst for the shoe retailer Foot Locker, one of your responsibilities is measuring store productivity and then reporting your conclusion back to management. Foot Locker uses sales per square foot as a measure of store productivity. While preparing your report for the second quarter results (Q2), you are able to determine that annual sales for last year ran at a rate of $406 per square foot. Therefore, $406 per square foot will be your sales estimate for the population of all Foot Locker stores during Q2. For your Q2 Sales Report, you decide to take a random sample of 64 stores. Using annual data from last year, you are able to determine that the standard deviation for sales per square foot for all 3,400 stores was $80. Therefore $80 per square foot will be your population standard deviation when compiling your Q2 report. Management has asked for the probability that your sample mean based on 64 stores is 1) within $15 and 2) within $5 of the population mean…
Question 4 A company that focuses on research and development (R&D) is interested in the relationship between profit and some variables that can explain profit variations. They want to predict the profits of some new and exciting projects because some are risky. The predictor variables considered are: RISK which is a company derived metric, R&D which is the research and development costs, REGION (WC, EC and KZN) and average per capita income (INCOME in R1000.00). The analyst decides to estimate the regression function with the following response function. (profit) = bo + b₁XRISK + b₂XR&D + b₂XWc + b₁XEC + b₂XINC Answer the following questions. 4.1. The analyst considered the fact that the effects of multicollinearity may influence some analytical and visual conclusions. Use the following results to show the process of establishing by using extra sums of squares whether there is that multicollinearity present or not. Explain and interpret in detail. SSR (X) = 2573.72, SSR(x₁) = 2600.7,…
C10. In Exercise 6, we examined the relationship between vears of education and hours ot television watched per day. We saw that as education increases, hours of television viewing decreases. The number of children a family has could also affect how much television is viewed per day. Having children may lead to more shared and supervised viewing and thus increases the number of viewing hours. The following SPSS output displays the relationship between television viewing (measured in hours per day) and both education (measured in years) and number of children. We hypothesize that whereas more education may lead to less viewing, the number of children has the opposite effect: Having more children will result in more hours of viewing per day. Sig. ,000 .000 a. What is the b coefficient for education? For number of children? Interpret each coefficient. Is the relationship between each independent variable and hours of viewing as hypothesized? b. Using the multiple regression equation with…
For the past 10 years, you have been observing the sales of your company since you embarked on an aggressive advertising campaign. You have been recording the amounts spent on advertising and the corresponding sales as follows: Year Advert (X) Sales (Y) 2001 10 44 2002 9 40 2003 11 42 2004 12 46 2005 11 48 2006 12 52 2007 13 54 2008 13 58 2009 14 56 2010 15 60 You would like to determine whether a relationship exists between your two variables of interest and therefore decide to run a regression. Specify the estimation model you will use (the econometric form). Identify the independent and the dependent variables. Use an econometric package of your choice (EViews, SPSS, STATA etc) to estimate the model. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). Re-write the specified model in (a) with values…
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the exponential smoothing method to forecast the number of sales to expect next month. Use the following value of alpha: 0.2. Month Demand Forecast 1 308 349.2 2 388 340.9 3 344 350.3 4 400 349.1 5 341 359.3
The data presented is a multiple logistic regression analysis and the models are shown below. In the models below, the data are coded as follows: p = the proportion of children with a diagnosis of ADHD, Child Exposed and Father’s Diagnosis are coded as 1 = yes and 0 = no. What is the odds ratio adjusted for father’s diagnosis? (Hint: Use only the appropriate model to find the odds ratio) (1) = –2.216 + 1.480 Child exposed (2) Father with diagnosis: = –1.665 + 1.297 Child exposed (3) Father without diagnosis: = –2.343 + 0.823 Child exposed (4) = –2.398 + 1.501 Child exposed + 0.906 Father’s diagnosis

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REGION L 2 3 1 = SALES (Y) ('000 gallons) 160 220 140 190 130 ADVERTISING EXPENSES (A) ($'000) 150 160 50 190 90 SELLING PRICE (P) ($/gallon) 15.00 13.50 16.50 14.50 1700 DISPOSABLE INCOME (M) ($'000) 19.0 17.5 14.0 21.0 155