Please help and explain thanks! Suppose we have the following dataset with one response to variable y and two predictorvariables x,and sxl14060221556125159672417970201927115200721421270142157811Please do needed steps to fit a MULTIPLE LINEAR REGRESSION model to the data set,present estimated linear regression equation from the steps done.

The dependent variable Y and two independent variables X1 and X2 are given as: y x1 x2 140 60…

Answered: Suppose we have the following dataset…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
A researcher records age in years (x) and systolic blood pressure (y) for volunteers. They perform a regression analysis was performed, and a portion of the computer output is as follows: ŷ = 3.3 +12.7x Coefficients (Intercept) X Estimate Std. Error Test statistic O Ho: B₁: = 0 Ha: B₁ 0 O Ho: B₁ = 0 Ha: B₁ 0 12.7 2.2 6.4 1.5 1.98 P-value Specify the null and the alternative hypotheses that you would use in order to test whether a positive linear relationship exists between x and y. 0.08 0.03
A particular article used a multiple regression model with the following four independent variables. y = error percentage for subjects reading a four-digit liquid crystal displayx1 = level of backlight (from 0 to 122 cd/m)x2 = character subtense (from .025 to 1.34)x3 = viewing angle (from 0 to 60)x4 = level of ambient light (from 20 to 1500 lx) The model equation suggested in the article is given below. (a) Assume that this is the correct equation. What is the mean value of y when x1 = 30, x2 = 0.6, x3 = 50 and x4 = 150?(b) What mean error percentage is associated with a backlight level of 40, character subtense of 0.6, viewing angle of 20, and ambient light level of 30?
Find the mean values of the variables X and Y for the following regression equation Regression line of Y on X = 2y-8=50 Regression line of X on Y=3y-2x=10
A b c
A researcher records age in years (x) and systolic blood pressure (y) for volunteers. They perform a regression analysis was performed, and a portion of the computer output is as follows: ŷ = 4.5+ 14.4x Coefficients (Intercept) x Estimate 4.5 Ho: B₁ = 0 H₁: B₁ > 0 Ho: B₁ = 0 Ha: B₁ <0 14.4 Ho: B₁ = 0 Ha: B₁ #0 Std. Error Test statistic 2.9 4.7 1.55 3.06 P-value Specify the null and the alternative hypotheses that you would use in order to test whether a linear relationship exists between x and y. 0.07 0
A sample consists of 500 houses sold in Karachi between Jamuary 2020 and December 2020. The multiple linear regression analysis is carried out to predict the house prices for investment in residential properties in Karachi, Pakistan. The output below is produced using SPSS. (300 words) Table: Coefficients Model Unstandardized Coefficients VIF Constant 14.208 5.736 Age of house -0.299 -2.322 1.58 Square footage of the house 0.364 2.931 1.71 Income of families in the area p.004 0.392 1.01 Transportation time to major markets -0.337 -2.619 1.90 R? = 0.67; DW = 2.08 Dependent Variable: House price (Pakistani rupees in Million) a) You are required to write the multiple regression equation. b) How would you interpret the above Output' of a regression analysis performed in SPSS? c) From the above results, what can you say about the nature of autocorrelation? d) Is there multicollinearity in regression? How do you know?
A researcher records age in years (x) and systolic blood pressure (y) for volunteers. They perform a regression analysis was performed, and a portion of the computer output is as follows: ŷ = 4.3 14.9x Coefficients (Intercept) X Estimate St 4.3 Ho: B₁ = 0 Ha: B₁ > 0 B1 O Ho: B₁ Ha: B₁ <0 = 0 14.9 B1 O Ho: B₁ = 0 0 Ha: B1 Std. Error Test statistic P-value 2.9 5.1 1.48 Specify the null and the alternative hypotheses that you would use in order to test whether a negative linear relationship exists between x and y. 2.92 0.08 0.01
The accompanying Minitab regression output is based on data that appeared in the article "Application of Design of Experiments for Modeling Surface Roughness in Ultrasonic Vibration Turning."+ The response variable is surface roughness (um), and the independent variables are vibration amplitude (um), depth of cut (mm), feed rate (mm/rev), and cutting speed (m/min), respectively. The regression equation is Ra = -0.972 - 0.0312a + 0.557d + 18.3f + 0.00282v Predictor Coef SE Coef Constant -0.9723 0.3923 -2.48 0.015 -0.03117 0.01864 -1.67 0.099 d 0.5568 0.3185 1.75 0.084 18.2602 0.7536 24.23 0.000 0.002822 0.003977 0.71 0.480 S = 0.822059 Source R-Sq = 88.6 R-Sq (adj) = 88.04 DS MS 0.000 Regression Residual Error 4 401.02 100.25 148.35 76 51.36 0.68 Total 80 452.38 (a) How many observations were there in the data set? observations (b) Interpret the coefficient of multiple determination. O 8.0% of the observed variation in feed rate can be explained by the model relationship with vibration…
The data below is for a hypothetical study investigating the Systolic Blood Pressure (SBP) of construction workers. The table shows respective measurements of 20 workers along with hours of work per day and the area of the city the work took place. [You can use Excel-Data Analysis – Regression or http://vassarstats.net/multU.html] SBP Hrs/day Area 109.6 7 east 107.4 8 east 140.3 9 east 146.5 12 east 98.2 6 east 137.8 9 east 124.1 10 east 113.2 8 east 127.8 9 east 125.3 8 east 108.5 6 west 181.3 13 west 137.4 10 west 146.2 10 west 142.4 9 west 123.7 8 west 129.6 8 west 143.6 9 west 160.7 11 west 148.3 9 west a) What is the regression equation? b) Interpret the meaning of the slopes in this problem. d) At the 0.05 level of significance, determine whether each independent variable makes a contribution to the…
Consider the accompanying data set of dependent and independent variables a. Perform a general stopwise regression using a 0.05 for the p-value to enter and to remove independent variables from the regression model b. Perform a residual analysis for the model developed in part a to verify that the regression conditions are met Click the icon to view the data a. Use technology to perform the general stepwise regression What is the resulting regression equation? Note that the coefficient is 0 for any variable that was removed or not significant -0.69 (050), (050)+(018) - X (Round to two decimal places as needed) • Data Table: y 63 43 51 49 40 42 23 37 30 27 20 31 FR 74 63 78 3534 52 44 47 35 17 15 20 17 Print X₂ 21 259. 15 9 38 18 17 5 40 27 30 33 x₂ 22 aadosa 2NNG 29 20 17 13 17 8 15 10 10 Done 1

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