PSDA
Timeline
WEEK 2: 21 March - 27 March
Chapter 1: Introduction to Statistics
1.1: Introduction1.1.1 Descriptive and Inferential Statistics.
1.1.2 Population and Sample.
1.2: Data
1.2.1 Data Analysis Process.
1.2.2 Data Sources (Primary and Secondary data).
1.2.3 Types of Data (Qualitative, Quantitative, Discrete, and Continuous data).
1.2.4 Data Scale and Measurement (Nominal, Ordinal, Interval, Ratio).
WEEK 3: 28 March - 3 April
Chapter 2: Data Description
2.1: Presenting Qualitative Data
2.1.1 Frequency Distributions, Bar and Pie Charts.
2.2: Presenting Quantitative Data
2.2.1 Frequency Distributions, Histograms, Stem-and-Leaf, Box Plot.
WEEK 4: 4 April - 10 April
Chapter 3: Descriptive Statistics
3.1: Measurement of Central Tendency
3.1.1 Mean, Median, Mode, Quartile, and Percentile.
3.2: Measurement of Dispersion
3.2.1 Range, Variance, Standard Deviation.
3.2.2 Skewness and Kurtosis.
WEEK 5: 11 April - 17 April
Lab Session: Introduction to Statistical Tools
Topic 1: Introduction
Topic 2: Basic Analysis
i. Frequencies Analysis.
ii. Descriptive Analysis.
WEEK 6: 18 April - 24 April
Chapter 4: Probability, Random Variables, and Probability Distributions
4.1: Probability
4.1.1 Overview of Probability.
4.2: Random Variables and Probability Distributions
4.2.1 Discrete and Continuous Random Variables.
4.2.2 Discrete and Continuous Variables Probability Distribution.
4.2.3 Binomial, Geometric, and Poisson Distributions.
4.2.4 Normal Distribution.
WEEK 8: 2 May - 8 May
Chapter 5: Hypothesis Testing
5.1: Point Estimation
5.1.1 Point Estimator
5.1.2 Interval Estimator
5.2: Hypothesis Testing for 1 Sample
5.2.1 Hypothesis Statement and Decision Rule
5.2.2 Errors of Decision
5.2.3 Hypothesis Testing
5.3: Hypothesis Testing for 2 Samples
5.3.1 Hypothesis Statement
5.3.2 Hypothesis Testing
WEEK 13: 6 June - 12 June
Chapter 6: Chi-Square Test and Contingency Analysis
6.1: Multinomial Experiment and Goodness-of-Fit Test
6.1.1 Multinomial Experiment
6.1.2 Goodness-of-Fit Test
6.2: One-way Contingency Table
6.2.1 Categories with equal frequencies/probabilities
6.2.2 Categories with unequal frequencies/probabilities
6.3: Two-way Contingency Table
6.3.1 Chi-Square Test of Independence
WEEK 14: 13 June - 19 June
Chapter 7: Correlation and Regression
7.1: Correlation
7.1.1 Correlation Analysis.
7.1.2 Pearson’s Correlation.
7.1.3 Spearman’s Correlation.
7.2: Regression
7.2.1 Types of Regression Models.
7.2.2 Population Linear Regression.
7.2.3 The Least Square Equation.
7.2.4 Coefficient of Determination.
7.2.5 Standard Error and Standard Deviation.
WEEK 15: 20 June - 26 June
Chapter 8: Analysis of Variance (ANOVA)
8.1: One-way ANOVA
8.1.1 ANOVA with Equal Sample Sizes.
8.1.2 ANOVA with Unequal Sample Sizes
8.2: Two-way ANOVA
8.2.1 Assumptions and Procedures.
REFLECTION ON PROBABILITY & STATISTICAL DATA ANALYSIS
This semester, I took Probability & Statistical Data Analysis, where I studied a variety of statistical analysis techniques that may be used by anybody or any company to analyze data. The acquired data can then be used to provide an answer or solution.
Projects one and two provided me with a wealth of experiences. I first discovered how to get the neighborhood to participate in our developed survey. Then, my group discusses and analyses its meaning, bar chart, and other survey-related data. Our first project taught us how to get to know one another, work successfully as a team, and compete against other teams.
Project two differs from project one in that secondary data were employed to conduct the analysis. By utilizing R Studio to analyze the data, this assignment will help me hone my abilities. There are several types of coding that we discovered in theoretical class and must use in this project. It gave me a lot of useful experiences. To make it simpler for me to do analysis fast and properly and offer the relevant information, this RStudio demands programming expertise. If you try to learn these abilities on your own, it takes an exceptionally long time. But having other group members who can exchange ideas and learn from one another can assist me in doing the responsibilities that have been given to me.
Additionally, when we had issues with datasets, our charming professor, Dr. Nor Azizah Binti Ali was helpful to us and helped me fall in love with this topic.
Finally, because the world is always tinkering with data, I am grateful that I can study this field. In the future, I hope it will enable me to excel as a software developer.
PROJECT 2
I have encountered several difficulties and acquired a great deal of fresh information while researching for this project. I started the study by looking online with two other members of my group for a sufficient set of data that we could evaluate. This is amusing since there are several datasets to pick from, each with a variety of variables. Only a few tests were available to us, including the Goodness of Fit Test, Chi-Square test for independence, Hypothesis Sampling, Correlation, and Regression.
I struggled to comprehend R Studio's code at the beginning of our project and figure out how to import my variables. No matter how large or little the issue is, Dr. NUR AZIZAH, my instructor for this course, is always incredibly helpful and clears up any misunderstandings. Thanks to Dr. NUR AZIZAH, I learned that it's OK to inquire about matters about which we are unsure. I now comprehend the project and statistics in general better. I'm now more motivated to study R programming and have a better understanding of data management and data analysis because of this.
I am thankful to be surrounded by incredible and motivating individuals, to put it briefly. I've learned a lot about topics that I previously didn't think I was capable of grasping thanks to working on a variety of projects. Additionally, it aids in boosting my self-assurance so I can handle more challenging situations in the future.