Want to explain these outputs? Dependent Variable: Fresh weightType III Sum ofSquaresSourceCorrected ModelInterceptTreatmentTests of Between-Subjects EffectsTypeTreatment TypeErrorTotalCorrected Total26.805203.76226.666.000.2498.928240.38235.7329114290300299Mean Square2.978203.7626.666.000.062.031F96.7426618.681216.540.0042.024Sig..000.000.000.948.091

ANOVA is the measure of mean significant between groups. If it has one categorical variable with…

Answered: Want to explain these outputs?

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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Please help me with this question if you know how to use the R software. I am very confused and my teacher tells me to type ?longley in the software but I still struggle to understand this question and answer it properly. Any help would be tremendously ap.p/reciated! Please don't answer it if you answered my question before!!!!>>
indlude answer(s) for part(s) A, B, C & D.
The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) to (c) below. Click the icon to view the data table. C... (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for female Find the least-squares regression line for males. ŷ=0x+0 (Round the slope to three decimal places and round the constant to the nearest integer as needed.) Data for licensed drivers by age and gender. 21-24 25-34 35-44 45-54 55-64 65-74 > 74 Number of Male Fatal Licensed Age Drivers (000s) < 16 12 16-20 6,424 6,914 18,068 20,406 Number of Number of Female Fatal Crashes Licensed (Males) Drivers (000s) 227 12 6,139 Crashes (Females) 77 2,113 1,534 5,180 5,016 6,816 8,567 17,664 2,780 7,990 20,047 2,742 19,984 14,441 8,386 5,375 19,898 14,328 8,194…
This dataset continues our saga of modeling the price of this popular Honda automobile. The dataset has now been cleaned to remove the columns with the dealership where the car was offered for sale and specific trim. (a) write out your model in econometric notation. Be very precise! (b) using the 93 observations in the dataset, estimate a model where price is a function of age, mileage and trim of the car. Be sure to avoid the dummy variable trap!! Fully report the results of your model. In this case, interpretation of the coefficients on the dummy variables is particularly important. (c) test the hypothesis that the specific trim does not affect the price of a Civic. Be sure to do all parts of the hypothesis test. (please fully describe steps if you are using Excel) Price Years Old KM EX EXT SE Sport Touring 6555 9 290363 0 0 0 0 0 9999 9 142258 0 0 0 0 0 10281 6 132644 0 0 0 0 0 12480 5 167125 0 0 0 0 0 12991 7 57398 0 0 0 0 0 12991 6 93046 0 0 0 0 0 12991…
Suppose when you add flexibility to your model by adding higher-order terms or more predictor variables that you begin to see the test or validation error (MSE or SSE evaluated on the test or validation set) begin to increase away from the training error. What kind of a problem are your models experiencing?
Suppose we obtain data on prices of big-screen televisions and estimate the following model: In(Price) = 4.06 + 0.06 * Size +0.23 * Wide + 0.34 * Plasma + 0.21 * LCD+0.09 * Memory where the dependent variable has been transformed, Size is the screen size measured in inches, Wide is a dummy variable equal to one if the television is a widescreen, Plasma is a dummy variable equal to one if the television is a Plasma screen, LCD is equal to one if the television is an LCD screen, and Memory is a dummy variable equal to one if the television has any memory card slots. What is the estimated price of a 42" Widescreen Plasma television with 2 memory card slots? Select one: O a. 7.24 O b. 7.33 1525.38 O d. 1394.09 • e 1881.83 Clear my choice
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Month Number of Accidents Jan 25 Feb 40 Mar 60 Apr 90 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): ŷ-0-0x Using least-squares regression, the forecast for the number of accidents that will occur in the month of May = accidents (enter your response as a whole number).
Is the following statement true or false? If false explain briefly. Some of the residuals from a least squares linear model will be positive and some will be negative. (Don't Hand writing in solution)
Fifty states Here is a data set on various measures of the50 United States. The Murder rate is per 100,000, HSGraduation rate is in %, Income is per capita income indollars, Illiteracy rate is per 1000, and Life Expectancy isin years. Find a regression model for Life Expectancywith three predictor variables by trying all four of thepossible models.a) Which model appears to do the best?b) Would you leave all three predictors in this model?c) Does this model mean that by changing the levels ofthe predictors in this equation, we could affect lifeexpectancy in that state? Explain. d) Be sure to check the conditions for multiple regres-sion. What do you conclude?
How can you apply PROBSTAT in your day to day basis? Give 5 examples.
if you score on your next statics says is converted to a z-score, which of these z-scores would you prefer?
F1.6 Question 18 please write on paper

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