Statistics-lab™可以为您提供metrostate.edu STAT501 Regression Analysis回归分析的代写代考和辅导服务！
STAT501 Regression Analysis课程简介
This graduate level course offers an introduction into regression analysis. A
Credits 3 researcher is often interested in using sample data to investigate relationships, with an ultimate goal of creating a model to predict a future value for some dependent variable. The process of finding this mathematical model that best fits the data involves regression analysis.
STAT 501 is an applied linear regression course that emphasizes data analysis and interpretation. Generally, statistical regression is collection of methods for determining and using models that explain how a response variable (dependent variable) relates to one or more explanatory variables (predictor variables).
PREREQUISITES
This graduate level course covers the following topics:
- Understanding the context for simple linear regression.
- How to evaluate simple linear regression models
- How a simple linear regression model is used to estimate and predict likely values
- Understanding the assumptions that need to be met for a simple linear regression model to be valid
- How multiple predictors can be included into a regression model
- Understanding the assumptions that need to be met when multiple predictors are included in the regression model for the model to be valid
- How a multiple linear regression model is used to estimate and predict likely values
- Understanding how categorical predictors can be included into a regression model
- How to transform data in order to deal with problems identified in the regression model
- Strategies for building regression models
- Distinguishing between outliers and influential data points and how to deal with these
- Handling problems typically encountered in regression contexts
- Alternative methods for estimating a regression line besides using ordinary least squares
- Understanding regression models in time dependent contexts
- Understanding regression models in non-linear contexts
STAT501 Regression Analysis HELP（EXAM HELP， ONLINE TUTOR）
- Indicate which of the four main regression objectives each of the following research issues would be:
a. What 4th-grade teacher did best given his/her students’ prior achievement?
b. What is the best guess for how many sailors the Navy will recruit this year?
c. Do people who swear have higher intelligence?
d. How much does keeping up with the material affect your eventual grade in the class?
e. Does keeping up with the material affect your eventual grade in the class?
- Suppose that, in a particular city, a regression of prices of homes sold in the prior year (price) on the number of bedrooms (bedrooms) and square feet (sqft) yields the following regression model:
$$
\widehat{\text { price }}=100,000+45,000 \times \text { bedrooms }+10 \times \text { sqft }
$$
a. Interpret the coefficient estimate on sqfi.
b. If a home had 2 bedrooms and was 1500 square feet, what would the predicted price be?
c. If that home had a selling price of $\$ 170,000$, what would be the residual?
d. How would you interpret the residual?
e. What is a possible reason for the residual being what it is?
3. From the book’s website, use the data set, democracy2. This is a data set of observations by country and year, with measures of democracy, life expectancy, and several other factors and outcomes. (see https://www.v-dem.net/). Use 1985 observations with condition1=1 (which is that there are non-missing values for all variables used in this regression).
a. Calculate the means and standard deviations of the following four variables to get a sense of the scales of the variable:
- life_exp (life expectancy)
- democracy (an index of the level of democracy in a county on a 0-1 scale)
- augeduc (average years of education)
- educgini (inequality Gini coefficient for years of education, on a 0-100 scale).
b. Regress life_exp on democracy, avgeduc, and educgini. Interpret the coefficient estimate on democracy.
c. For a country with life-exp $=60$, democracy $=0.5$, avgeduc $=10$, and educgini $=50$, what is the predicted value of the dependent variable and the residual?
d. Interpret the predicted value and the residual.
e. Interpret the $R^2$.
f. Add urbanpct (the percent of the population living in an urban area) as an explanatory variable to the model. Interpret the change in $R^2$ after adding urbanpct.
4. From the book’s website, use the data set, income. Estimate three separate models, with income as the dependent variable. Include just one of the three explanatory variables (educ, afqt, age) in each model. Which explanatory variable explains the greatest amount of variation in income? How did you arrive at your answer?
Textbooks
• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.
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