Reference no: EM132490721

FIT5197 - Modelling for data analysis - Monash University

Confidence Interval, Hypothesis Testing, Data Mining Models

Objectives
This assignment assesses your understanding of Confidence Interval, Hypothesis Testing, and Data Mining Models.

Question 1: Central Limit Theorem
Centrl Limit Theorem belives that the sampling distribution of the mean of samples has a particular property. Regardless of the population that we want to make inference about it, if we draw many samples, the sampling distribution of the sample mean is always symmetric and bell-shaped. Please program to simulate the Central Limit Theorem in different pupolation distributions (at least 3) and sampling sizes (at least 3). Totally 9 trails.

Question 2: Hypothesis Testing

(1). A steel-making factory want to know if the introduced new method can increase its productivity. The staffs recorded 10 productivity results of the old method and the new method, respectively. The results are given as follow. Explain your t-test findings.

Old Method: 78.1 72.4 76.2 74.3 77.4 78.4 76.0 75.5 76.7 77.3 <br> New Method: 79.1 81.0 77.3 79.1 80.0 79.1 79.1 77.3 80.2 82.1 <br>

Note that samples are independent with each other and come from normal distributions N(μ1, σ2) and N(μ2, σ2) where μ1,μ2 and σ2 are unknown.

old <- c(78.1,72.4,76.2,74.3,77.4,78.4,76.0,75.5,76.7,77.3)
new <- c(79.1,81.0,77.3,79.1,80.0,79.1,79.1,77.3,80.2,82.1)
(2). Do the old and new samples truely come from two distributions of the same variance?

Question 3: Linear Regression and Anova

Use the dataset ‘Q3 Data.txt' (which is tab delimited) on different brands of cigarettes - you want to predict CO (Carbon Monoxide output) given the other variables.

1. Fit all seven possible linear models with CO as the dependent variable (i.e. with all possible sets of independent variables except for no independent variables) and summarise the results in a table.

2. Identify what you think is the best model for predicting CO and explain why you think it is good.

3. Include a summary of diagnostic checks that you try for your best model (Residuals versus Fitted, Normal Q-Q, scale-location, and residuals vs leverage.).

Question 4: Logistic Regression
You are required to predict affair with Logistic Regression in this task. The used dataset comes from a survey conducted by Psychology Today in 1969 which contains 601 observations on 9 variables.A detailed data description is shown as below.

affairs
numeric. How often engaged in extramarital sexual intercourse during the past year? 0 = none, 1 = once, 2 = twice, 3 = 3 times, 7 = 4-10 times, 12 = monthly, 12 = weekly, 12 = daily.

gender
factor indicating gender.

age
numeric variable coding age in years: 17.5 = under 20, 22 = 20-24, 27 = 25-29, 32 = 30-34, 37 = 35-39, 42 = 40-44, 47 = 45-49, 52 = 50-54, 57 = 55 or over.

yearsmarried
numeric variable coding number of years married: 0.125 = 3 months or less, 0.417 = 4-6 months, 0.75 = 6 months-1 year, 1.5 = 1-2 years, 4 = 3-5 years, 7 = 6-8 years, 10 = 9-11 years, 15 = 12 or more years.

children
factor. Are there children in the marriage?

religiousness
numeric variable coding religiousness: 1 = anti, 2 = not at all, 3 = slightly, 4 = somewhat, 5 = very.

education
numeric variable coding level of education: 9 = grade school, 12 = high school graduate, 14 = some college, 16 = college graduate, 17 = some graduate work, 18 = master's degree, 20 = Ph.D., M.D., or other advanced degree.

occupation
numeric variable coding occupation according to Hollingshead classification (reverse numbering).

rating
numeric variable coding self rating of marriage: 1 = very unhappy, 2 = somewhat unhappy, 3 = average, 4 = happier than average, 5 = very happy.

# install.packages("AER")
data(Affairs,package="AER")

summary(Affairs)
## affairs gender age yearsmarried children
## Min. : 0.000 female:315 Min. :17.50 Min. : 0.125 no :171
## 1st Qu.: 0.000 male :286 1st Qu.:27.00 1st Qu.: 4.000 yes:430
## Median : 0.000 Median :32.00 Median : 7.000
## Mean : 1.456 Mean :32.49 Mean : 8.178
## 3rd Qu.: 0.000 3rd Qu.:37.00 3rd Qu.:15.000
## Max. :12.000 Max. :57.00 Max. :15.000
## religiousness education occupation rating
## Min. :1.000 Min. : 9.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:14.00 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :16.00 Median :5.000 Median :4.000
## Mean :3.116 Mean :16.17 Mean :4.195 Mean :3.932
## 3rd Qu.:4.000 3rd Qu.:18.00 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :5.000 Max. :20.00 Max. :7.000 Max. :5.000
1.Data Pre-Processing. (E.g. removal of null values; numeralization of factor features; split of training and test set with a ratio of 8:2, etc.)

2.Built a logistic regression model on the training data. You need to determine which feature to use based on the p values analysis.

3.Evaluate the trained model on the test set.

Attachment:- Modelling for data analysis.zip