Publications by Laboratory Exercise No. 5
Stat 136 Lab Exercise No. 4
INSTRUCTION: Provide details of all calculations. Problem 1 A School District Supervisor is interested to verify the claim that female school heads/principals are better at managerial skills than their male counterparts. He obtained data on management skills of random samples of 13 male and 12 female school heads from four school districts us...
1825 sym 2 tbl
Stat 142 Laboratory Exercise No. 4
Stat 142 (Time Series Analysis) Laboratory Exercise No. 4 Author Norberto E. Milla, Jr. Consider wmurders data set in the fpp2 package. It contains the number of women murdered each year (per 100,000 standard population) in the United States. By studying appropriate graphs of the series in R, find an appropriate ARIMA( p, d, q) model for thes...
867 sym
Econ 115s Second Long Exam (Part 2)
Econ 115s (Introduction to Econometrics) Second Long Exam (Part 3) Published April 26, 2023 Load the gpa3 data set of the wooldridge package. To know more about the variables in the data set, open this link gpa3. Fit the following model: \[ cumgpa = \beta_0 + \delta_0 female + \beta_1 sat + \beta_2 hsperc + \beta_3 tothrs + u \] Interpret e...
1644 sym
Stat 142 Long Exam 2 (Part 3)
Stat 142 (Time Series Analyis) Long Exam No. 2 (Part 3) Author Norberto E. Milla, Jr. Published April 24, 2023 The data set DepartmentStoreSales.csv contains quarterly sales for a department store over a 6-year period. Plot and describe the time series. What components are present in the data? [4 points] Use the first 20 quarters as the tra...
1237 sym
Stat 136 Long Exam 2 (Part 3)
Stat 136 (Bayesian Statistics) Long Exam No. 2 (Part 3) Author Norberto E. Milla, Jr. Published April 23, 2023 Problem No. 1: The number of defects per 10 meters of cloth produced by a weaving machine has the Poisson distribution with mean \(\lambda\). You examine 100 meters of cloth produced by the machine and observe 71 defects. a) Your p...
1879 sym
Econ 115s Lesson 2.2 Multiple Linear Regression (Inference)
Stat 115s (Introduction to Econometrics) Lesson 2.2- Multiple Linear Regression: Inference Author Norberto E. Milla, Jr. Published April 21, 2023 1 Sampling distributions of OLS estimators To make statistical inference (hypothesis tests, confidence intervals), in addition to expected values and variances we need to know the sampling distribu...
19626 sym Python (8733 sym/24 pcs) 1 img 2 tbl
Stat 142 Lab Exercise No. 3
Stat 142 (Time Series Analysis) Laboratory Exercise No. 3 The data set fancy (in the fma package) concerns the monthly sales figures of a shop which opened in January 1987 and sells gifts, souvenirs, and novelties. The shop is situated on the wharf at a beach resort town in Queensland, Australia. The sales volume varies with the seasonal populati...
1395 sym
Econ 115s Lesson 3.1 (Qualitative Explanatory Variables in Regression Analysis)
Table of contents 1 Introduction 2 Single dummy independent variable 3 Creating dummy variables in R 4 Adding quantitative variables 5 More than one dummy variables 6 Allowing for different slopes Stat 115s (Introduction to Econometrics) Lesson 3.1- Qualitative Explanatory Variables in Regression Analysis Author Norberto E. Milla, Jr. P...
13742 sym 3 img
Stat 142- Lesson 2.3 (Time series regression models)
Table of contents 1 Introduction 2 The linear model 2.1 Simple linear regression model 2.2 Multiple linear regression model 2.3 Assumptions 3 Least squares estimation 3.1 Example 3.2 Fitted values 3.3 Goodness-of-fit 4 Evaluating the regression model 4.1 ACF plot of residuals 4.2 Other useful plots of residuals 4.3 Outliers and i...
31283 sym Python (4752 sym/20 pcs) 13 img 1 tbl
Stat 136 Lab Exercise No. 4
INSTRUCTION: Provide details of all calculations. Problem 1 You are the statistician responsible for quality standards at a cheese factory. You want the probability that a randomly chosen block of cheese labelled “1 kg” is actually less than 1 kilogram to be 1% or less. The distribution of the weight (in grams) of blocks of cheese produce...
1713 sym