Statistics for Computer Programmers Week 1

Course Information

• Course title: Statistics for Computer Programmers
• Code: MATH1236
• Instructor's name: Harpreet Kaur
• Session Number: 1

Course Information

• Course Code: MATH1236
• Course Title: Statistics for Computer Programming
• Lesson week: 1

Course Information

• Course Title: Statistics for Computer Programmers
• Course Code: MATH1236
• Delivery Type: Onsite
• Program Coordinator: Ridhi Patel
• Program Coordinator Contact: [email protected]
• Credit Value: 3
• Developed by: Harpreet Kaur
• Approved by: Subagini Manivannan
• Start Date: 12-Dec-2024
• End Date: 19-April-2024
• Days: Friday
• Start Time: 18:30:00
• End Time: 21:30:00

Course Information

• Week 1: Familiarize with the course outline and TLP, introduction to learning objectives and outcome, familiarize with evaluation methods and weightage. Statistics, Type of Statistics, Level of Measurement, Design Experiment, Pictorial representation.
• Assessments and Activities: Lecture, ppt, and practice questions
• Learning Objectives: Demonstrate knowledge of statistical language. Categorize data by type and level of measurement. Design experiments without computer software, organize and represent data with frequency distributions.

Learning Outcomes

• Present data with appropriate statistical language, both with and without computer software
• Analyze univariate data
• Analyze bivariate data, and interpret the linear relationship between the two variables
• Determine probabilities by use of classical and empirical probabilities
• Evaluate probabilities and statistics for discrete probability distribution
• Perform statistical calculations by use of the normal distribution
• Predict confidence intervals and required sample sizes

Evaluations and Weights

• Quizzes: 12.00%
• Tests: 88.00%
• Total: 100.00%

Learning Objectives and Outcomes

• Present data with appropriate statistical language, both with and without computer software
• Analyze univariate data
• Analyze bivariate data, and interpret the linear relationship between the two variables
• Learning Objectives: Demonstrate knowledge of statistical language, categorize data by type and level of measurement, design experiments, organize and represent data with frequency distributions, histograms, stem and leaf plots, summarize data, apply empirical rule for a normal distribution, use z scores and quartiles
• Determine probabilities by use of classical and empirical probabilities, evaluate probabilities and statistics for discrete probability distribution, perform statistical calculations by use of the normal distribution, and predict confidence intervals and required sample sizes
  • Establish sample spaces, calculate probabilities of compound events, and conditional probability
  • Construct probability distributions, compute mean, variance, standard deviation for discrete random variable, verify calculations, compute area under standard normal dist for z-value, and compute probabilities by use of standard normal variable transformation
  • Construct confidence interval for population mean, calculate sample size for confidence interval, and construct confidence interval for a small sample

TLP

• Week 1: Familiarize with Course Outline and TLP, Introduction to Learning Objectives and Outcomes, Familiarize with Evaluation Methods and Weightage
• Week 2: Graphing, Frequency Distributions, Stem-and-Leaf Plots, Measures of Location, Measures of Dispersion, Measure of Relative Standing, Using Excel for calculations
• Week 3: Measures of Position and Relative Standing, Empirical Rule, Z-Scores, Using Excel for Distribution and Measures of Position, Introduction of R-Studio
• Week 4: Test 1, based on week 1,2 and 3 contents
• Week 5: Descriptive Statistics using R-Studio, Correlation Coefficient, Data Analysis, scatter plot for bivariate data using Excel for correlation coefficients
• Week 6: Least Square Regression Line, Coefficient of determination, distribution, measures of correlation and regression line, predictions using R-Studio
• Week 7: Test-2 as Midterms, based on W-5 and W-6 contents
• Week 8: Reading Week (no classes)
• Week 9: Probability, rule of addition and multiplication, conditional probability
• Week 10: Discrete distribution, mean, variance, standard deviation, binomial probability, calculations using R-studio
• Week 11: Standard Normal Distribution, variable transformation, extrapolation of data
• Week 12: Test 3, based on week 9 and 10 contents
• Week 13: Confidence interval for small and large samples, standard deviation, population calculation without or with Excel and R-studio
• Week 14: Confidence interval for small and large sample with and without standard deviation and for population with Excel and R-Studio
• Week 15: Test 4, based on week 11, 13 and 14 contents

Evaluation Details

• Quiz 1 (6%): Week 6
• Quiz 2 (6%): Week 11
• Test 1 (22%): Week 4
• Test 2 (22%): Week 7
• Test 3 (22%): Week 12
• Test 4 (22%): Week 15
• Total: 100%

Software Requirement

• Microsoft Excel and R-Studio
• Statistical data input and result interpretation

Data Sets, Elements, Variables, and Observations

• Data are collected facts and figures
• Elements - entities on which data are collected
• Variables - characteristic of interest
• Observation - set of measurements obtained for an element
• Data set - collection of data, observations for a study
• Variables examples - stock exchange, annual sales, share

Levels of Measurement

• Nominal - labels or names
• Ordinal - order/rank is meaningful
• Interval - interval between observations is meaningful, fixed unit of measure
• Ratio - interval data and ratio of two values is meaningful, zero value indicates absence

Categorical and Quantitative Data

• Categorical - labels or names
• Quantitative – numerical and measurable

Univariate Data

• Observations on a single variable

Bivariate Data

• Observations on two variables

Multivariate Data

• Observations on more than one variable

Branches of Statistics

• Descriptive statistics - summarizing data
• Inferential statistics - generalizing from a sample to a population

Experimental Design

• Detailed plan to collect data that identifies causal relationships
• Controlled conditions to understand causal relationships between variables

Pictorial and Tabular Methods in Descriptive Statistics

• Visual (bar graphs, scatter diagrams) and numerical summary measures (mean, standard deviations, correlations)

Frequency

• Number of times a value occurs in the data
• Cumulative relative frequency - accumulation of relative frequencies, add previous relative frequencies to current row values

Course Topics

• Familiarize with LMS Canvas features
• Familiarize with the topics and their learning objectives and outcomes
• Familiarize with evaluation methods and weightage
• Software required, including Statistics, Type of Statistics, Level of Measurement, Design Experiment, Pictorial Representation and Constructing Frequency Distributions

This quiz covers the introductory concepts of the Statistics for Computer Programmers course. Students will familiarize themselves with the course outline, learning objectives, evaluation methods, and foundational statistics concepts. Key topics include types of statistics, levels of measurement, and experimental design.