`Correlation and Regressiona) Use the least square equation to predict income given ages=21 & 45 & experience=0 & 5.b) Compute for multiple correlation coefficient.Age3137284233253540Experience41543322Income3540041200450004030036800432004160038700

Solution-: We want to find, (1) Use the least square equation to predict income given ages=21 &…

Answered: Correlation and Regression a) Use the…

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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Course: Statistics - Lineal Regression A company dedicated to sale of services wants to carry out a study on impact of use of web during working hours. Age (years) Worker's services Labor (years) Web time connected (weekly) Monthly salary (Thousands of $) 22 1 22 254.1 43 8 13 749.2 45 9 11 713.8 47 10 14 874.5 53 11 8 709.6 50 11 10 933.9 56 13 9 1,050.3 59 12 6 874.9 47 10 8 797.4 53 13 12 1,021.7 47 10 11 782.5 55 15 6 1,091.2 48 10 9 1,054.0 49 10 11 748.4 48 12 12 847.0 50 11 9 978.2 39 7 13 601.9 47 9 11 860.1 48 10 12 769.8 49 10 10 740.3 44 8 12 683.8 49 11 10 786.6 48 11 10 858.0 47 8 12 559.5 48 9 10 908.5 58 11 6 797.0 43 8 13 719.4 a) Make a scatter plot and indicate relationship between variable…
ev The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 28 33 36 42 45 Number of Bids 1 7 8 9 10 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Table Copy Data Next
A concept used when interpreting partial correlation coefficients to indicate that a control variable appears to explain the relationship between two other variables completely A concept used in multiple regression and partial correlation to indicate that the effect of one independent variable on the dependent variable is different depending on the value of another independent variable A value that is computed for each independent variable in a multiple regression equation and is used in the equation as a multiplier to the value of the corresponding independent variable A statistical method that uses the least-squares method to produce an equation to predict the Z score of one variable using the Z scores of two or more other variables A value that describes the percentage of variability in the dependent variable that is explained by two or more independent variables A measure of the relationship between two variables without consideration of any other…
The consumption function captures one of the key relationships in economics. It expresses consumption as a function of disposal income, where disposable income is income after taxes. The attached file "Regression-Dataset1" shows data of average US annual consumption (in $) and disposable income (in $) for the years 2000 to 2016. a)what is the Pearson correlation value between income and consumption? Select one: Ⓒa. 0.978 b. 0.109 c. None of the above d. 0.791
Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative?
Triples and Home Runs The data below show the number of three-base hits (triples) and the number of home runs hit during the season by a random sample of MLB teams. Triples 33, 37, 27, 29, 18, 21 Home runs 305, 345, 245, 265, 155, 185 The correlation coefficient for the data is r= 1 and a=.01. Should regression analysis be done? The regression analysis should not be done or The regression analysis should be done. Find the equation of the regression line. Round the coefficients to at least three decimal places, if necessary y'= a+bx a= b= find y' when x= 28. Round the answer to at least three decimal places, if necessary y'=
3. Correlation and linear regression
Spearman Rank Coefficient of Correlation
Refrigerator prices are affected by characteristics such as whether or not the refrigerator is on sale, whether or not it is listed as a Sub-Zero brand, the number of doors (one door or two doors), and the placement of the freezer compartment (top, side, or bottom). The table below shows the regression output from a regression model using the natural log of price as the dependent variable. The model was developed by the Bureau of Labor Statistics. Variable Coefficient Standard Error t Statistic Intercept 5.5800 0.12871 43.35 Sale price −0.0733 0.0223 −3.29 Sub-Zero brand 1.0424 0.1472 7.08 Total capacity (in cubic ft) 0.06894 0.00563 12.25 Two-door, freezer on bottom 0.05198 0.07124 0.73 Two-door, side freezer Base Two-door, freezer on top −0.3595 0.03694 −9.73 One door with freezer −0.6585 0.1274 −5.17 One door, no freezer −0.8435 0.1565 −5.39 (a) Write the…
Spearman Rank Coefficient of Correlation
Income and housing revisited In Chapter 6, Exercise 32, we learned that the Office of Federal Housing Enter-prise Oversight (OFHEO) collects data on various as-pects of housing costs around the United States. Here’s a scatterplot (by state) of the Housing Cost Index (HCI)versus the Median Family Income (MFI) for the 50 states.The correlation is r = 0.65. The mean HCI is 338.2,with a standard deviation of 116.55. The mean MFI is$46,234, with a standard deviation of $7072.47.a) Is a regression analysis appropriate? Explain.b) What is the equation that predicts Housing Cost Indexfrom median family income?c) For a state with MFI = +44,993, what would be thepredicted HCI?d) Washington, DC, has an MFI of $44,993 and an HCIof 548.02. How far off is the prediction in c) from theactual HCI?e) If we standardized both variables, what would be theregression equation that predicts standardized HCIfrom standardized MFI?f) If we standardized both variables, what would be theregression equation that…
Data Bank Hide ( correlation = undefined Show regression line Create a scatterplot with at least 8 points where the correlation is between 0.5 and 0.7 by clicking and dragging points from the Data Bank onto the graph. Enter the correlation coefficient displayed in the applet. correlation coefficient: Now add another data point, in the upper-left corner of the scatterplot. This causes the correlation coefficient Then click and drag this to point down to the lower-left corner of the scatterplot. Take note of this new correlation coefficient and how it changed when you dragged the point down.

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Age	31	37	28	42	33	25	35	40
Experience	4	1	5	4	3	3	2	2
Income	35400	41200	45000	40300	36800	43200	41600	38700

Correlation and Regression a) Use the least square equation to predict income given ages=21 & 45 & experience=0 & 5. b) Compute for multiple correlation coefficient. Age 31 37 28 42 33 25 35 40 Experience 4 1 5 4 3 3 2 2 Income 35400 41200 45000 40300 36800 43200 41600 38700