4 A certified financial planner would like to examine the relationship between the age of her clientsand the total amount of money they have in their investment accounts. Theages of clients and corresponding account balances for a sample of 15 clientsappears in the table. Use the regression analysis output from Excel todetermine if there is a positive linear relationship between age of client andinvestment account balance. Test using a significance level of .01.BalanceAge ($1000s)3438578744129CoefficientsStandard Error t stat183.893.52256342InterceptX variable-226.6950770-1.232.8710.104442005443039556745329764323076427m ib4318870546275Linear Regression and Correlation

Given: X Y 34 38 57 87 44 129 56 342 50 770 44 200 39 54 55 430 67 453 29 76…

Answered: A certified financial planner would…

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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Q2
In a quantitative analysis where widowed people suffering with bereavement suffer with a poor quality of life. Would Widowed Individuals be the Unit of Analysis? The quality of life be the dependent variable, therefore making bereavement the independent variable?
The Equal Opportunity Commission is investigating questions around unequal pay rates and discriminatory remuneration in various industries. The Pay Equity tab in the excel workbook contains information on 100 employees from a particular industry. Information includes: Salary ($) Gender Years of Education Years of Experience Division of employment Age Run a multiple regression analysis looking at the relationship between salary and years of education and years of experience. What proportion of variation in salary can be explained by these two variables? Conduct a test of the overall significance of the model. Test both the Education and Experience variables separately. Do both contribute to explaining the variation in salaries? Write out the estimated equation and interpret all coefficients. Salary (in $) Education (years) Experience (years) 20860 11 4 30200 16 1 31240 12 1 36860 12 8 44760 14 4 46690 19 3 47400 15 1 47880 14 3 50620 13 6 50690 14 4…
Identify the type of data that would be used when the variable of interest is most-watched TV show.
Define the term Sample Correlation? why it is used in linear equation?
The number of megapixels in a digital camera is one of the most important factors in determining picture quality. But, do digital cameras with more megapixels cost more? The following data show the number of megapixels and the price ($) for 10 digital cameras(Consumer Reports, March 2009). Use these data to develop an estimated regression equation that can be used to predict the price of a digital camera given the number of megapixels. Brand and Model Megapixels Price (S) Canon PowerShot SD1100 IS Casio Exilim Card EX-510 8 180 200 230 10 Sony Cyber-shot DSC-T70 Pentax Optio M50 Canon PowerShot G10 120 470 15 8 Canon PowerShot A590 IS Canon PowerShot El 140 180 10 12 Fujifilm FinePix FOOFD Sony Cyber-shot DSC-W170 Canon PowerShot A470 310 10 250 110
Use the Financial database from “Excel Databases.xls” on Blackboard. Use Total Revenues, Total Assets, Return on Equity, Earnings Per Share, Average Yield, and Dividends Per Share to predict the average P/E ratio for a company. Use Excel to perform a forward selection regression analysis. Assume a 5% level of significance. Based on your final model, what is the p-value from the test of the overall model? Write your answer as a number and round to 3 decimal places. Company Type Total Revenues Total Assets Return on Equity Earnings per Share Average Yield Dividends per Share Average P/E Ratio AFLAC 6 7251 29454 17.1 2.08 0.9 0.22 11.5 Albertson's 4 14690 5219 21.4 2.08 1.6 0.63 19 Allstate 6 20106 80918 20.1 3.56 1 0.36 10.6 Amerada Hess 7 8340 7935 0.2 0.08 1.1 0.6 698.3 American General 6 3362 80620 7.1 2.19 3 1.4 21.2 American Stores 4 19139 8536 12.2 1.01 1.4 0.34 23.5 Amoco 7 36287 32489 16.7 2.76 3.1 1.4 16.1 Arco Chemical 2 3995 4116 6.2 1.14 6.1 2.8 40.4…
You do a survey where you ask people both their age and their income. You want to see if there is a relationship between these two variables, and you want to create an equation you can use to predict someone’s age from his/her income. What kind of hypothesis test or analysis should you do?a. One-Factor, Independent-Measures ANOVAb. Two-Factor ANOVAc. Correlation/Regressiond. Chi-Square Goodness of Fit
Define the Test a slope for significance?
The output of a different regression analysis on the effect of radio advertising spending, television advertising spending and online advertising spending on sales is given below. For this study they explored 3 different types of advertising spending (radio, television and online). They modelled it as y = ao + a1¤1 + az¤2 + az¤3. The table below gives the summary output of this regression analysis. Regression Analysis: Sales versus Spend Analysis of Variance Source DF s MS F-Value P-Value Regression d freg 23,986.44 MSR F-val p-val Error dferr 558.52 MSE Total 38 Find the Mean Squares for Regression (MSR) for this problem. Give your answer to 3 decimal places.
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
Interpret the slope of the least square regression line in content

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