The following information was gathered by the managerial accountant to estimate the 2nd quarter's utility cost:Direct laborMonthsUtility CosthoursJanuaryP8,5201809FebruaryP7,6281421MarchP5,712912AprilP6,7461102Summation of X5,244Summation of Y28,606Summation of XY38,895,504Summation of X-squared7,337,870Using the regression analysis, what is the estimated cost-function for the 2nd quarter?O Y = 3,207 + 3xO Y = 12,828 + 3xO Y = 12,828 + 2xO Y = 9,621 + 3xPrevious

For the given data, we need to find the regression equation of estimated cost function for the 2nd…

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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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Consider the following passage: I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime. Which writing mistakes, if any, did…
If you want to reproduce the analysis with similar data, what is standing in your way in producing the same results?
Body Fat. Where we considered the regression of percentage of body fat on nine body measurements: height, weight, hip, forearm, neck, wrist, triceps, scapula, and sup. Describe and discuss problems that could have arisen in the collection of the data for this regression analysis.
Personal wealth tends to increase with age as older individuals have had more opportunities to earn and invest than younger individuals. The following data were obtained from a random sample of eight individuals and records their total wealth (Y) and their current age (X). Data provided in the image. a. State the estimated regression line and interpret the slope coefficient.b. What is the estimated total personal wealth when a person is 50 years old?c. What is the value of the coefficient of determination? Interpret it. d. Test whether there is a significant relationship between wealth and age at the 10%significance level. Perform the test using the following six steps. Step 1. Statement of the hypothesesStep 2. Standardised test statisticStep 3. Level of significanceStep 4. Decision. Step 5. Calculation of test statistic. Step 6. Conclusion Please solve the d) part only.
Shelia’s doctor is concerned that she may suffer from gestational diabetes (high blood glucose levels during pregnancy). There is variation both in the actual glucose level and in the blood test that measures the level. A patient is classified as having gestational diabetes if her glucose level is above 136 milligrams per deciliter (mg/dl) one hour after ingesting a sugary drink. Shelia’s measured glucose level one hour after the sugary drink varies according to the Normal distribution with μ = 125 mg/dl and σ = 11 mg/dl. a)What are the mean and standard deviation of the distribution of sample proportion if measurements are made instead on 40 separate days? b)If measurements are made on 40 separate days and the mean result is compared with the criterion 140 mg/dl, what is the probability that Shelia is diagnosed as having gestational diabetes? c)Let’s consider what could happen if we took 40 separate measurements from Shelia. What is the blood glucose level L such that the probability…
A vocational counselor uses the number of days without employment to predict her clients' feelings of self efficacy, measured on a scale of 1 to 5, with higher numbers meaning that clients feel more secure in their job related abilities. The slope of the regression line is –1.02. Which statement is the best interpretation for this finding? a. For every 1-point increase in self-efficacy, there is an associated decrease in the number of days of unemployment. b. For every additional day of unemployment, there is an associated decrease in self-efficacy of 1.02 points. c. The least number of days a person can be unemployed and still feel self-efficacious is 3.98 points d. The decrease in self-efficacy of 1.02 points is caused by each additional day of unemployment
Give an example of how a nonrepresentative sample can severely affect a regression analysis.
The following is a 7-month-demand-history for a certain product. Assuming that linear trend appears, develop the equation of regression line. Round values of coefficients to two decimal places. Use the equation to prepare a forecast for December 2004.
The marketing manager of "ABC Tapes" wants to assess the competitiveness of the company's product in the market. Table 1 represents the prices and quantities of blank cassette tapes sold in January 2016 and January 2020. TABLE 1 2016 2020 Length of tapes Price Quantity Price Quantity 30 minutes R 4.00 32 R 5.60 40 60 minutes R 4 30 150 R 6.15 190 90 minutes R 4.60 100 R 7.40 130 Using 2016 as base and calculate for 2020 the following: Determine the Laspeyres price index: a) 149.492 Ob) 149.367 Oc) 66.949 d) 66.893
urgent.......
21. Which of the following statements is true regarding the sources of variation present in an analysis of regression? SSy is partitioned into variation explained by the regression model and residual variation. If most of the variability in Y is associated with residual variation, then X predicts Y. There are three sources of variation in an analysis of regression: regression variance, residual variance, and error variance. Regression variation measures variability in X, whereas residual variation measures variability in Y.
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