Data Management and Analysis with Stata.

Data Management and Analysis with Stata., available at $74.99, has an average rating of 4.57, with 68 lectures, based on 381 reviews, and has 1339 subscribers.

You will learn about Get familiar with environment of Stata Understand the syntax structure and the five fundamental commands Create new variables, replace and recode values in existing variables Handle missing data, apply variable and value labels, and work with string variables Compute and interpret descriptive statistics including mean, standard deviation, range, skewness, kurtosis and percentiles Compute and interpret correlations, one and two-sample t tests, construct multivariate means graphs Analysis of variance (ANOVA) Scatter plots, simple linear regression, multiple linear regression, regression with dummy variables Interpreting regression output and hypothesis testing This course is ideal for individuals who are Anyone involved in Data Management and Data Analysis It is particularly useful for Anyone involved in Data Management and Data Analysis.

Enroll now: Data Management and Analysis with Stata.

Summary

Title: Data Management and Analysis with Stata.

Price: $74.99

Average Rating: 4.57

Number of Lectures: 68

Number of Published Lectures: 67

Number of Curriculum Items: 68

Number of Published Curriculum Objects: 67

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Get familiar with environment of Stata
Understand the syntax structure and the five fundamental commands
Create new variables, replace and recode values in existing variables
Handle missing data, apply variable and value labels, and work with string variables
Compute and interpret descriptive statistics including mean, standard deviation, range, skewness, kurtosis and percentiles
Compute and interpret correlations, one and two-sample t tests, construct multivariate means graphs
Analysis of variance (ANOVA)
Scatter plots, simple linear regression, multiple linear regression, regression with dummy variables
Interpreting regression output and hypothesis testing

Who Should Attend

Anyone involved in Data Management and Data Analysis

Target Audiences

Anyone involved in Data Management and Data Analysis

Note: The course is COMPLETE now.

This course, extended over seven sections, provides a comprehensive introduction to Stata and Statistics. The aim of the course is to teach all the variables, and the relevant Stata commands, used in Statistics. These variables are nominal, ordinal, interval, and ratio variables.

There are two alternative ways to undertake the course.

1. If you have a basic understanding of Stata, you can directly start from section 3, which teaches Data Management. You should then proceed to section 4 on Descriptive Statistics, which is common to all types of research. Section 5 analyses a relationship and interprets it between Nominal/Ordinal variables. Examples of these types of variables are gender, race, employment status, ethnicity, levels of satisfaction, customer service quality, hair color, and religion among others. Section 6 investigates a relationship and interprets it between the Nominal/Ordinal variableand the Interval/Ratio variable.Section 7finds an effect of one Interval/Ratio variable on another Interval/Ratio variable. Examples of these types of variables are age, income, prices, exam scores, temperature, distance, and area among others. Note:If you adopt this strategy, you may need to go back to the second section, if you have any trouble understanding a particular Stata command in sections 3, 4, 5, 6, and 7. The advantage of this strategy is you will study the more important content first.

2.Alternatively, you can follow the exact order of the course, starting from section 1 and then proceeding to the next section until you reach the section 7. If you follow this strategy, make sure you do not give up in the middle of the course. The research shows that, and this course is not an exception, some students do not complete the entire course. In this course, the first 3 sections are meant to prepare you for the next 4 sections. Therefore, quitting in the first half of the course will deprive you of the intended benefits.

Whichever alternative you choose, you must download the resources and practice with me during the lectures. In addition, you must attempt all exercises given at the end of each section.

Captions:Each video/lecture is accompanied by accurate captions to enhance your comprehension of the course contents.

Resources:You will be provided with a separate data set for each section to practice with me during the lectures. You will also be given a separate data set to attempt the exercises at the end of each section. You will obtain five do-files, one on data management, and the remaining four on data analysis. The only prerequisites for the course are to install Stata on your computer and remain committed.

Good Luck!

Course Curriculum

Chapter 1: Knowing Stata

Lecture 1: The Course Overview

Lecture 2: Obtaining Stata

Lecture 3: Section One Resources

Lecture 4: Stata's Main Screen and Multiple Windows

Lecture 5: Browse, Edit, Enter, and Import Data

Lecture 6: Types of Variables

Lecture 7: Changing Working Directory

Chapter 2: The Fundamental Commands

Lecture 1: The Section Two Resources

Lecture 2: The Basic Command Structure

Lecture 3: Practice: Stata's Command Syntax

Lecture 4: The Tabulate Command

Lecture 5: Practice: The Tabulate Command

Lecture 6: The Summary Command

Lecture 7: The Generate Command

Lecture 8: The Replace Command (1)

Lecture 9: The Replace Command (2)

Lecture 10: The Recode Command

Lecture 11: The Rename, Drop, Keep, and Display Commands

Chapter 3: Data Management and Do-File

Lecture 1: The Section Three Resources

Lecture 2: Motivation for using Do-File

Lecture 3: Opening and Using Do-File

Lecture 4: Practice with Do-File

Lecture 5: The Variable and Value Labels

Lecture 6: Missing Data (1)

Lecture 7: Missing Data (2)

Lecture 8: String Variables (1)

Lecture 9: String Variables (2)

Lecture 10: End of String Variables and Saving Results

Chapter 4: Descriptive Statistics

Lecture 1: The Section Four Resources

Lecture 2: Descriptive Statistics for Nominal and Ordinal Variables

Lecture 3: Producing Histogram

Lecture 4: Summary Statistics for Interval and Ratio Variables

Lecture 5: Variance, Skewness, Kurtosis, Range and Percentiles

Lecture 6: Tabstat Command to control Quantity and Output

Chapter 5: Analyzing a Relationship between Nominal/Ordinal Variables

Lecture 1: The Section Five Resources

Lecture 2: Necessary Background for Hypothesis Testing and Types of Variables

Lecture 3: Getting Data Ready for Hypothesis Testing (1)

Lecture 4: Getting Data Ready for Hypothesis Testing (2)

Lecture 5: Chi-Square Test for Hypothesis Testing

Lecture 6: Measures of Association / Relationship between Nominal / Ordinal Variables

Lecture 7: Elaboration (To analyse the effect of an additional variable)

Chapter 6: Analyzing a Relationship between Nominal/Ordinal and Interval/Ratio Variables

Lecture 1: The Section Six Resources

Lecture 2: Differentiate Variables and the Main Research Question

Lecture 3: Handling Legitimately Skipped Cases

Lecture 4: Constructing and Interpreting a Confidence Interval

Lecture 5: One Sample t Test

Lecture 6: Two Samples t Test (1)

Lecture 7: Two Samples t Test (2)

Lecture 8: Analysis of Variance (ANOVA)

Chapter 7: Regression Analysis (Analyzing a Relationship between Interval/Ratio Variables)

Lecture 1: The Section Seven Resources

Lecture 2: Scatterplot

Lecture 3: Correlation (1)

Lecture 4: Correlation (2)

Lecture 5: Understanding Regression

Lecture 6: Simple (Bivariate) Linear Regression (1)

Lecture 7: Simple (Bivariate) Linear Regression (2)

Lecture 8: Multiple Linear Regression

Lecture 9: Predicted/Fitted Values and Residuals

Lecture 10: A Dichotomous Variable in Multiple Linear Regression

Chapter 8: Bonus Videos

Lecture 1: Section Eight Resources

Lecture 2: List, Summarize and Scatter Graph

Lecture 3: Fitted Values, Residuals and Regression Line

Lecture 4: A Quadratic Regression Model (1)

Lecture 5: A Quadratic Regression Model (2)

Lecture 6: A Log-Linear Regression Model

Lecture 7: A Regression using Indicator Variables (1)

Lecture 8: A Regression using Indicator Variables (2)

Instructors

Ihsan Ullah
Assistant Professor, Finance

Rating Distribution

1 stars: 3 votes
2 stars: 7 votes
3 stars: 26 votes
4 stars: 139 votes
5 stars: 206 votes

Frequently Asked Questions

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