4 +10+Lab+More+SLR

Looking at this output in the photo, we know that the  proportion of the variation in mileage is accounted for by the linear relationship with the weight of the car is 43.8 % and that the estimate of the regression standard deviationis is 6.78.  Determine the equation of the Least Squares Regression Line and what the interpretation of the slope for weight of car is?  Then performing a T-test, determine what the value of the t stastitic is if we wanted to test if there is a significant straight-line relationship between the weight and the mileage of a car.

please interpret the model

Tire pressure (psi) and mileage (mpg) were recorded for a random sample of seven cars of thesame make and model. The extended data table (left) and fit model report (right) are based on aquadratic model   What is the predicted average mileage at tire pressure x = 31?

Fit these three regression models and then discuss the similarities and differences between them, particularly as relates to slope estimates (use CI’s) and R2. Also address why this is a “special case” and we wouldn’t necessarily expect to see these model characteristics for a typical dataset. a) Additive model including both predictors (output attached) b) Model including only Moisture (output attached) c) Model including only Sweetness BrandLiking = 68.62 + 4.38 Sweetness Term                     95% CI                     P-ValueConstant          (50.16, 87.09)                 0.000Sweetness       (-1.46, 10.21)                   0.130 S                            R-sq                     R-sq(adj)10.8915                15.57%                    9.54%

last two subparts please

We estimate a simple regression Grade-B,+B,Effort + u where the red line represents Qur estimate for Grade relative to Effort, and the dots represent our data points. Which OLS assumption is violated in this regression?

Given the relatively small sample size, we can characterize the left-hand plot as a random cloud of points in a rough horizontal band and the right-hand plot as points that lie reasonably close to the 45° line. Briefly describe what this tells us about the simple linear regression model assumptions in this case.   The largest absolute standardized residual for this simple linear regression analysis is 1.298, while the highest leverage is 0.430. Briefly describe what this tells us about potentially influential observations in this analysis.

Use the given dataset*note: Gender takes on a value of 1 if the student is male, and 0 otherwise Estimate a linear regression model relating overall grade weighted average (OGWA) of student to their gender, available internet speed (mbps) and previous term’s grade weighted average (lgwa)a. Interpret the slope coefficients (discuss their values and statistical significance)b. Are the coefficients jointly statistically significant? Explain your answer.c. How much of the variability of the overall grade weighted average is explained by the variability of the model?

Use RStudio to fit a simple linear regression model to the data below. Please submit a copy of your code and any appropriate output (a photo/screenshot will be sufficient). 300? Is the fitted linear regression model What is the 95% prediction interval for xo = appropriate for this data, and which assumption appears to be violated if not? dataset <- data.frame ( с (294, 247, 267, 358, 423, 311, 450, 534, 438, 688, 630, 709, 627, 1021, 615, 700, 999, 1250, 1015, 850, 980, 1650, 1025, 1200, 1500), у 3 с (30, 32, 37, 44, 47, 49, 56, 62, 68, 80, 84, 88, 97, 97, 100, 106, 109, 112, 117, 128, 130, 135, 160, 180, 210) X = %3D

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?

(BIOSTATISTICS) In this question , What characteristics are associated with BMI? Use simple and multivariable linear regression analysis to complete the following table relating the characteristics listed to BMI as a continuous variable. Before conducting the analysis, be sure that all participants have complete data on all analysis variables. If participants are excluded due to missing data, the numbers excluded should be reported. Then, describe how each characteristic is related to BMI. Are crude and multivariable effects similar? What might explain or account for any differences? Outcome Variable: BMI, kg/m2   Characteristic   Regression Coefficient Crude Models p-value Regression Coefficient Multivariable Model P-value   Age, years 0.0627 <0.001-0.02155 0.004 Male sex -0.580 <0.001-09884 <0.001 Systolic blood pressure, mmHg 0.0603 <0.0010.05716            <0.001 Total serum cholesterol, mg/dL 0.0113 <0.0010.00638…