Podcast
Questions and Answers
What is a census?
What is a census?
Which of the following best describes a 'variable' in a data analysis context?
Which of the following best describes a 'variable' in a data analysis context?
In a study, the 'elements' are best defined as:
In a study, the 'elements' are best defined as:
What is the 'observation' in data analysis?
What is the 'observation' in data analysis?
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If a data set contains 10 elements and each element has 4 variables, how many total data values are there?
If a data set contains 10 elements and each element has 4 variables, how many total data values are there?
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Which scenario exemplifies a population in a statistical study?
Which scenario exemplifies a population in a statistical study?
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Which of these is NOT typically a reason that a census might be impractical?
Which of these is NOT typically a reason that a census might be impractical?
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How are data sets typically described?
How are data sets typically described?
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What is one primary function of a histogram?
What is one primary function of a histogram?
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How does a frequency polygon differ from a histogram?
How does a frequency polygon differ from a histogram?
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In the histogram example provided, which class interval has the highest frequency?
In the histogram example provided, which class interval has the highest frequency?
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What do the heights of the bars in a histogram represent?
What do the heights of the bars in a histogram represent?
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What must be true for a histogram to be considered correctly formatted?
What must be true for a histogram to be considered correctly formatted?
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What percentage of students at De Anza College were Asian in Fall Term 2007?
What percentage of students at De Anza College were Asian in Fall Term 2007?
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Which ethnic group had the least representation at De Anza College in Fall Term 2007?
Which ethnic group had the least representation at De Anza College in Fall Term 2007?
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How many students worked for 5 hours according to the frequency table?
How many students worked for 5 hours according to the frequency table?
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What is the total percentage of Hispanic and Black students at De Anza College?
What is the total percentage of Hispanic and Black students at De Anza College?
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How many total students were included in the sample for the work hours frequency table?
How many total students were included in the sample for the work hours frequency table?
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Which ethnic group comprised 5.3% of the student population at De Anza College in Fall Term 2007?
Which ethnic group comprised 5.3% of the student population at De Anza College in Fall Term 2007?
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What was the total number of students recorded in the census at De Anza College?
What was the total number of students recorded in the census at De Anza College?
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Which of the following groups had a frequency of 3 in the work hours table?
Which of the following groups had a frequency of 3 in the work hours table?
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What type of data is represented by the observations of a single variable?
What type of data is represented by the observations of a single variable?
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Which of the following exemplifies bivariate data?
Which of the following exemplifies bivariate data?
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In a multivariate data set, which of the following statements is true?
In a multivariate data set, which of the following statements is true?
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What is the primary purpose of descriptive statistics?
What is the primary purpose of descriptive statistics?
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Which of the following is an example of univariate data?
Which of the following is an example of univariate data?
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If an engineer records the lifetime of a component and the reason for its failure, what type of data is this?
If an engineer records the lifetime of a component and the reason for its failure, what type of data is this?
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Which example represents categorical data?
Which example represents categorical data?
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What does a bivariate dataset typically consist of?
What does a bivariate dataset typically consist of?
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What is one primary goal of using experimental design in psychological research?
What is one primary goal of using experimental design in psychological research?
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In a manufacturing environment, what is a common goal of experimental design?
In a manufacturing environment, what is a common goal of experimental design?
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Which aspect of experimental design is crucial for ensuring generalizability to the real world?
Which aspect of experimental design is crucial for ensuring generalizability to the real world?
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What does maximizing data reliability in experimental design aim to achieve?
What does maximizing data reliability in experimental design aim to achieve?
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What is a key component for developing an effective experimental design?
What is a key component for developing an effective experimental design?
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Which of the following factors is considered when trying to isolate a factor's true effect in an experiment?
Which of the following factors is considered when trying to isolate a factor's true effect in an experiment?
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In a medical experiment, what aspect might researchers be primarily focused on?
In a medical experiment, what aspect might researchers be primarily focused on?
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What is a crucial aspect that needs to be managed to ensure a trustworthy experimental design?
What is a crucial aspect that needs to be managed to ensure a trustworthy experimental design?
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What is the relative frequency of the data value '5' in the table?
What is the relative frequency of the data value '5' in the table?
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If the total number of students in the sample was 40, what would the relative frequency of the data value '2' be?
If the total number of students in the sample was 40, what would the relative frequency of the data value '2' be?
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What is the sum of the frequencies in the table?
What is the sum of the frequencies in the table?
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If the relative frequency of a data value is 0.10, what is the frequency of that data value?
If the relative frequency of a data value is 0.10, what is the frequency of that data value?
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Why do relative frequencies add up to 1?
Why do relative frequencies add up to 1?
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Study Notes
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
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Description
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.
