hw problem & answersQuestion #1Question #2QuestQuestion #33. (A) Perform a multiple regression analysis on the above data to see if household income andpopulation can be used to predict future sales. (B) Predict sales for the next three years based onthe income and population projections listed below.(fill in the blanks on the first sheet)AverageNumber ofHouseholdWidgetsYearHouseholdsIncomeSold198039.534,0005,500198139.834,6005,600198240.441,0005,900198343.138,0005,800198443.239,0006,100198544.042,0006,900198644.544,0006,300198746.249,0007,300198846.546,0006,700198946.851,0006,900199047.048,0007,200199147.147,0007,100199247.350,0007,100199347.751,0007,400199448.053,0007,600199548.154,0007,900199648.455,0007,300199748.553,0008,000199849.057,0008,500

Data Preparation: Organize your data into a table with columns for Year, Number of Households,…

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Century 21 Accounting Multicolumn Journal

11th Edition

ISBN:9781337679503

Author:Gilbertson

Publisher:Gilbertson

Chapter17: Financial Statement Analysis

Section: Chapter Questions

Problem 1AP

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Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? Is there an indication of a seasonal pattern? b. Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. c. Compute the quarterly forecasts for next year.
1. The annual net income of MAC Industries since 2005 is given below. Years t (since 2005) Net Income 1 4 6 12 15 $1,500 $2,500 $2,875 $2,850 $2,250 a. Create a scatter plot that includes your curve of best fit on top of the data. You should try several functions and make your choice based on best r-squared value. b. Find the year that Net Income was at its highest point. c. What was the income in that year?
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Which one of the following best describes an arithmetic average return? Multiple Choice A. Total return divided by N − 1, where N equals the number of individual returns B. Average compound return earned per year over a multiyear period C. Total compound return divided by the number of individual returns D. Return earned in an average year over a multiyear period E. Positive square root of the average compound return
Editing Voice Sensitvity Edtor 7. Suppose we have the following annual sales data for an automobile dealership: Year Sales Trend 2011 121 1 2012 187 2013 165 2014 134 2015 155 2016 167 6. 2017 200 7 2018 206 2019 221 2020 231 10 We want to forecast sales for 2021 and 2022 using either a simple trend model or a model using the RMSE measure discussed in lecture. Once this model has been determined, provide actual forecasts for 2021 and 2022. Report the two RMSE values in your submission along with the actual forecasts. Submit your Excel file used to create these answers. quadratic trend model. Use a within sample forecasting technique to determine the nonle become sick
Analyze the influence of factors on the level of return on sales (ROS). Choose the proper method of factor analysis. Year 200A Year 200B Sales, $'000 56000 58421 Cost of goods sold, $'000 48000 50024
How do you calculate net sales? Also... Do you only look at current year data or do you factor previous year data and current year? Thx
Which one of the following is defined as the average compound return earned per year over a multiyear period? Multiple Choice A Geometric average return B Variance of returns C Standard deviation of returns D Arithmetic average return E. Normal distribution of returns
The fitted regression is Sales = 920 - 27.5 Price. (0-1) If Price = 1, what is the prediction for Sales? (Round your answer to 1 decimal place.) Sales (a-2) Choose the correct statement. A decrease in price decreases sales. O An increase in price increases sales. O An increase in price decreases sales. (b) If Price = 23, what is the prediction for Sales? (Round your answer to the nearest whole number.) Sales (c) Choose the right option. The intercept is meaningful as sales will be maximized when price is zero. O The intercept is not meaningful as a zero price is both unrealistic and unobserved.
Consider again the example introduced in Section 4.5 of a credit card company that has a database of information provided by its customers when the customers apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3). Figure 4.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? d. Recall the multiple regression in Figure 4.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts a, b, and c? What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? e. Add the coefficients of determination for the simple linear regression in parts a, b, and c, and compare the result to the coefficient of determination for the multiple regression model in Figure 4.23. What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? f. Add age, a dummy variable for gender, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 4.23. Code the dummy variable for gender as 1 if the customers gender is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?
Fill in the blanks with the number that corresponds to the correct word or phrse below: 1. Deflation 2. GDP deflator 3. Consumer price index (CPI) 4. base 5. Labor statistics 6. Employment cost index 7. dollar 8. Producer price index 9. International price index 10. Consumer expenditure survey 11. Percentage change 12. indices Price are created to calculate an overall average change in relative prices over time. To convert the money spent on the market basket of goods, to an index number, economists arbitrarily choose one year to be the year, or starting point from which we measure changes in prices. The year, by definition, has an index number equal to 100. The inflation rate is not derived by subtracting the index numbers, but rather through the calculation. Index numbers have no signs or other units attached to them. The most commonly cited measure of inflation in the United States is the The Bureau of is responsible for the computation of the Consumer Price Index. is a national…
Use the following information to determine the Prior Year and Current Year trend percents for net sales using the Year as the base year Enter the answers thousands of dollars.

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