1. Let's say X is a main predictor (or exposure) of your interest and M is a potential mediator,and Y is an outcomeModel 1: Y = Bo(1) + tX+ E(1)Model 2: Y= Bo(2) + t’X+ BM+ ɛ(2)Model 3: M Воз) + аХ + £(3)Indicate the p-value of each regression parameter in the table below. Please fill the p-value in thetable whether it must be greater or less than 0.05 for confirming M as a mediator as per Baron-Kenny's Criteria.t' (full mediationmodel)t' (partial mediationтodel)p-value

In this problem, we are investigating the potential mediating effect of variable M in the…

Answered: 1. Let's say X is a main predictor (or…

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter1: Functions

Section1.2: The Least Square Line

Problem 5E

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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?
For the following table of data. x 1 2 3 4 5 6 7 8 9 10 y 0 0.5 1 2 2.5 3 3 4 4.5 5 a. draw a scatterplot. b. calculate the correlation coefficient. c. calculate the least squares line and graph it on the scatterplot. d. predict the y value when x is 11.
We have data on Lung Capacity of persons and we wish to build a multiple linear regression model that predicts Lung Capacity based on the predictors Age and Smoking Status. Age is a numeric variable whereas Smoke is a categorical variable (0 if non-smoker, 1 if smoker). Here is the partial result from STATISTICA. b* Std.Err. of b* Std.Err. N=725 of b Intercept Age Smoke 0.835543 -0.075120 1.085725 0.555396 0.182989 0.014378 0.021631 0.021631 -0.648588 0.186761 Which of the following statements is absolutely false? A. The expected lung capacity of a smoker is expected to be 0.648588 lower than that of a non-smoker. B. The predictor variables Age and Smoker both contribute significantly to the model. C. For every one year that a person gets older, the lung capacity is expected to increase by 0.555396 units, holding smoker status constant. D. For every one unit increase in smoker status, lung capacity is expected to decrease by 0.648588 units, holding age constant.
Researchers are trying to assess the effectiveness of a new blood pressure medication. Using their data, they calculate a simple linear regression model that predicts systolic blood pressure (SBP) in terms of BP Meds (where 0 means the new medication is given and 1 means a placebo is give). The results are shown in the last row of the middle 2 columns of the table below. The researchers believe that Age, Gender, and BMI might be confounders. They calculate simple linear models for each of these variables as shown in the table where SBP is the response variable in each model. Then they calculate a multiple regression model that predicts SBP in terms of all 4 variables. The results are given in the last 2 columns on the table. Based on these results, is the association between BP meds and SBP confounded by Age, Gender or BMI? Provide a brief (1-2 sentences) explanation. Simple Models Multiple Regression b p b p Age 1.03 <.0001 0.86…
The manufacturer of Beanie Baby dolls used quarterly price data for 2012/-2020/V (t = 1, ..., 36) and the regression equation Pt= a + bt+c₁D1 t + c2 D2 + + c3 D3 t to forecast doll prices in the year 2021. Pt is the quarterly price of dolls, and D1, D2t, and D3+ are dummy variables for quarters I, II, and III, respectively. DEPENDENT VARIABLE: PT R-SQUARE P-VALUE ON F 0.0001 OBSERVATIONS: F-RATIO 76.34 STANDARD 36 0.9078 PARAMETER VARIABLE ESTIMATE ERROR T-RATIOP-VALUE INTERCEPT 24.0 6.20 3.87 0.0005 T 0.8 0.240 3.33 0.0022 D1 -8.0 2.60 -3.08 0.0043 D2 -6.0 1.80 -3.33 0.0022 D3 -4.0 0.60 -6.67 0.0001 The estimated quarterly increase in price is and the estimated annual increase in price is Multiple Choice O O $1.50; $6.00 $1.40; $4.00 $0.60; $2.40 $0.80; $3.20 None of the choices are correct.
A) A linear regression has a =6 and b=5 what is y predicted as when x=9? B) A linear regression has b=3 and a=4.What is the predicted Y for x=7?
The manufacturer of Beanie Baby dolls used quarterly price data for 2012/- 2020/V (t = 1, ..., 36) and the regression equation Pt = a + bt + c₁D1 t + c2 D2t + c3D3 t to forecast doll prices in the year 2021. Pt is the quarterly price of dolls, and D1, D2t, and D3 are dummy variables for quarters I, II, and III, respectively. DEPENDENT VARIABLE: PT R-SQUARE P-VALUE ON F 0.0001 OBSERVATIONS: 36 0.9078 F-RATIO 76.34 STANDARD ERROR PARAMETER ESTIMATE T-RATIOP-VALUE VARIABLE INTERCEPT 24.0 6.20 3.87 0.0005 T 0.8 0.240 3.33 0.0022 D1 -8.0 2.60 -3.08 0.0043 D2 -6.0 1.80 -3.33 0.0022 D3 -4.0 0.60 -6.67 0.0001 In any given year price tends to vary from quarter to quarter as follows: Multiple Choice O O O P₁ > P|| > P||| > PIV PI> PIV> Pill > PII Pll > Pill > PIV> PI Pill > PI>PII > PIV PIV> Pill > PII > PI
What does it mean when the interacting term x1x2 is added to the general linear regression model to account for two predictive variables, x1 and x2, and outcome variable y? O The effect of one unit change in x1 is the same as the effect of one unit change in x2 on the outcome variable y. The effect of one unit change in x1 on the outcome variable y is statistically significant only when x2 is nonzero, and vice versa. O The effect of one unit change in x1 on the outcome variable y depends on the value of x2, and vice versa. O The predictive variables x1 and x2 do not influence the value of the outcome variable y individually. Only changes of the product (i.e., x1X2) of x1 ảnd x2 will impact y.
1ee 19 20 21 22 23 24 25 Price ars) QUESTION 6 A medical rescarcher found a significant relationship among a person's age x, chloresterol level x2, sodium level of the blood x3, and systolic blood pressure y. The regression equation is 97.3+0.700x+21&-299x3, Predict the systolic blood pressure of someone who is 31 years old and has a chloresterol level of 195 milligrares per deciliter and a sodium blood level of 142 milliequivalents per liter. O 171 O-24 O 128 O 85,087 QUESTION 7 MacBook Air SO DII F3 FS F6 & 4 W R T Y D H K V
The manufacturer of Beanie Baby dolls used quarterly price data for 2012/- 2020/V (t = 1, ..., 36) and the regression equation Pt= a + bt+c₁D1t + c2D2t + c3D3t to forecast doll prices in the year 2021. Pt is the quarterly price of dolls, and D1, D2t, and D3+ are dummy variables for quarters I, II, and III, respectively. DEPENDENT VARIABLE: PT R-SQUARE P-VALUE ON F 0.0001 OBSERVATIONS: F-RATIO 76.34 STANDARD 36 0.9078 PARAMETER ESTIMATE VARIABLE ERROR T-RATIOP-VALUE INTERCEPT 24.0 6.20 3.87 0.0005 T 0.8 0.240 3.33 0.0022 D1 -8.0 2.60 -3.08 0.0043 D2 -6.0 1.80 -3.33 0.0022 D3 -4.0 0.60 -6.67 0.0001 At the 2 percent level of statistical significance, is there a statistically significant trend in the price of dolls? Multiple Choice Yes, because 0.0022 0.02. Yes, because 0.800 > 0.02. Yes, because 0.240 > 0.02. Yes, because 3.33 > 0.02.
overleaf…/Page 7(b) Finally, the researcher is interested in examining the regression model for knowledge, attitudeand practices towards the COFLU-20. The following model was developed to forecastindividual practices towards COFLU-20 using knowledge and attitude scores.P = α + β K + δ Awhere P = Practice towards COFLU-20 scoreK = Knowledge towards COFLU-20 scoreA = Attitude towards COFLU-20 scoreThe data are processed using MINITAB and the output in Exhibit 1 below was obtained:Exhibit 1The regression equation is *************Predictor Coef SE t-ratio PConstant 4.755 0.462 10.282 0Knowledge 0.8 0.039 2.055 0.041Attitude 0.024 0.6 0.393 0.695R-sq = 81.5%Analysis of VarianceSOURCE DF SS MS F PRegression 2 38.06 19.03 2.284 0.104Error 297 2474.887 8.333Total 299 2512.947(i) Identify the dependent variable(s) and the independent variable(s).[(ii) Is δ significant?Show proof of the testing process used to arrive at your decision.State what this means in terms of attitude and practices.…
(e) Find the least-squares regression line treating square footage as the explanatory variable.

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