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Simple Regression presentation is a partial fulfillment to the requirement in PA 297 Research for Public Administrators, presented by Atty. Gayam , Dr. Cabling and Mr. Cagampang
Simple linear regression
Simple linear regression
Maria Theresa
Multiple Linear Regression
Multiple Linear Regression
Vamshi krishna Guptha
Multiple Linear Regression
Multiple Linear Regression
Multiple Linear Regression
Indus University
Multiple linear regression
Multiple linear regression
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Avjinder (Avi) Kaler
A set of slides to illustrate the idea of simple linear regression. Could be used as introductory materials before teaching linear regression.
Simple Linear Regression (simplified)
Simple Linear Regression (simplified)
Haoran Zhang
Linear regression
Linear regression
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Babasab Patil
Simple Linier Regression
Simple Linier Regression
dessybudiyanti
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
Multiple linear regression
Multiple linear regression
James Neill
Recommended
Simple Regression presentation is a partial fulfillment to the requirement in PA 297 Research for Public Administrators, presented by Atty. Gayam , Dr. Cabling and Mr. Cagampang
Simple linear regression
Simple linear regression
Maria Theresa
Multiple Linear Regression
Multiple Linear Regression
Vamshi krishna Guptha
Multiple Linear Regression
Multiple Linear Regression
Multiple Linear Regression
Indus University
Multiple linear regression
Multiple linear regression
Multiple linear regression
Avjinder (Avi) Kaler
A set of slides to illustrate the idea of simple linear regression. Could be used as introductory materials before teaching linear regression.
Simple Linear Regression (simplified)
Simple Linear Regression (simplified)
Haoran Zhang
Linear regression
Linear regression
Linear regression
Babasab Patil
Simple Linier Regression
Simple Linier Regression
dessybudiyanti
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
Multiple linear regression
Multiple linear regression
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Conducting a t-test for the slope.
10 11 t-test for slope
10 11 t-test for slope
christjt
A follow-up to the previous chi-squared slide show.
10 11 chi-squared ii
10 11 chi-squared ii
christjt
10 11 chi-squared
10 11 chi-squared
christjt
Matched pair and 2 sample
Matched pair and 2 sample
christjt
10 11 one sample t-tests
10 11 one sample t-tests
christjt
A discussion of Type I and Type II Errors in hypothesis testing.
10-11 Errors
10-11 Errors
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10 11 One Prop z-intervals
10 11 One Prop z-intervals
christjt
A detailed look at all fundamentals of conducting a hypothesis test for a single proportion.
10 11 One Proportion Z-tests Detailed
10 11 One Proportion Z-tests Detailed
christjt
The basics surrounding hypothesis testing. A refresher on the normal approximation to the binomial model is provided. A follow-up presentation will make it formalized.
10 11 Hypothesis Testing Mechanics
10 11 Hypothesis Testing Mechanics
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10 11 binomial distributions
10 11 binomial distributions
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Geometric distributions - calculating probability, determining expected value, formulas, details and context.
10-11 Geometric Distributions
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Explorations of operations between random variables, and the impact on the mean and standard deviation.
Expected Value & Variances Add - Updated
Expected Value & Variances Add - Updated
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The class notes on exploring variances adding, along with the expected value for the sum and difference of two independent random variables.
10-11 Expected Value & Variances Add
10-11 Expected Value & Variances Add
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Practice with some elements of probability. Determining independence, using conditional probability to fit with a multiplication rule. Creation of tree diagrams.
10 11 Probability Practice
10 11 Probability Practice
christjt
Building an understanding of conditional probability. Making the idea of reversing conditioning about restricting all possible outcomes to one condition. Determining if events are independent from one another.
10 11 Independence
10 11 Independence
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A discussion of independent events and non-independent events, which is followed by notes on disjoint and non-disjoint events.
10-11 Probability Wrapup
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Lessons on some more formal rules in calculating probability.
10 11 Probability Formalization
10 11 Probability Formalization
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Lessons on conditional probability and the multiplication rule. No formal rules have been set, but examples are provided.
10 11 Multiplication Rule and Conditional Probability
10 11 Multiplication Rule and Conditional Probability
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First day of probability basics.
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10 11 Probability Basics
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Conducting a t-test for the slope.
10 11 t-test for slope
10 11 t-test for slope
christjt
A follow-up to the previous chi-squared slide show.
10 11 chi-squared ii
10 11 chi-squared ii
christjt
10 11 chi-squared
10 11 chi-squared
christjt
Matched pair and 2 sample
Matched pair and 2 sample
christjt
10 11 one sample t-tests
10 11 one sample t-tests
christjt
A discussion of Type I and Type II Errors in hypothesis testing.
10-11 Errors
10-11 Errors
christjt
10 11 One Prop z-intervals
10 11 One Prop z-intervals
christjt
A detailed look at all fundamentals of conducting a hypothesis test for a single proportion.
10 11 One Proportion Z-tests Detailed
10 11 One Proportion Z-tests Detailed
christjt
The basics surrounding hypothesis testing. A refresher on the normal approximation to the binomial model is provided. A follow-up presentation will make it formalized.
10 11 Hypothesis Testing Mechanics
10 11 Hypothesis Testing Mechanics
christjt
10 11 binomial distributions
10 11 binomial distributions
christjt
Geometric distributions - calculating probability, determining expected value, formulas, details and context.
10-11 Geometric Distributions
10-11 Geometric Distributions
christjt
Explorations of operations between random variables, and the impact on the mean and standard deviation.
Expected Value & Variances Add - Updated
Expected Value & Variances Add - Updated
christjt
The class notes on exploring variances adding, along with the expected value for the sum and difference of two independent random variables.
10-11 Expected Value & Variances Add
10-11 Expected Value & Variances Add
christjt
Practice with some elements of probability. Determining independence, using conditional probability to fit with a multiplication rule. Creation of tree diagrams.
10 11 Probability Practice
10 11 Probability Practice
christjt
Building an understanding of conditional probability. Making the idea of reversing conditioning about restricting all possible outcomes to one condition. Determining if events are independent from one another.
10 11 Independence
10 11 Independence
christjt
A discussion of independent events and non-independent events, which is followed by notes on disjoint and non-disjoint events.
10-11 Probability Wrapup
10-11 Probability Wrapup
christjt
Lessons on some more formal rules in calculating probability.
10 11 Probability Formalization
10 11 Probability Formalization
christjt
Lessons on conditional probability and the multiplication rule. No formal rules have been set, but examples are provided.
10 11 Multiplication Rule and Conditional Probability
10 11 Multiplication Rule and Conditional Probability
christjt
First day of probability basics.
10 11 Probability Basics
10 11 Probability Basics
christjt
Summary of lessons on sampling and surveying from the 2010-2011 school year.
10-11 Sampling and Surveying
10-11 Sampling and Surveying
christjt
More from christjt
(20)
10 11 t-test for slope
10 11 t-test for slope
10 11 chi-squared ii
10 11 chi-squared ii
10 11 chi-squared
10 11 chi-squared
Matched pair and 2 sample
Matched pair and 2 sample
10 11 one sample t-tests
10 11 one sample t-tests
10-11 Errors
10-11 Errors
10 11 One Prop z-intervals
10 11 One Prop z-intervals
10 11 One Proportion Z-tests Detailed
10 11 One Proportion Z-tests Detailed
10 11 Hypothesis Testing Mechanics
10 11 Hypothesis Testing Mechanics
10 11 binomial distributions
10 11 binomial distributions
10-11 Geometric Distributions
10-11 Geometric Distributions
Expected Value & Variances Add - Updated
Expected Value & Variances Add - Updated
10-11 Expected Value & Variances Add
10-11 Expected Value & Variances Add
10 11 Probability Practice
10 11 Probability Practice
10 11 Independence
10 11 Independence
10-11 Probability Wrapup
10-11 Probability Wrapup
10 11 Probability Formalization
10 11 Probability Formalization
10 11 Multiplication Rule and Conditional Probability
10 11 Multiplication Rule and Conditional Probability
10 11 Probability Basics
10 11 Probability Basics
10-11 Sampling and Surveying
10-11 Sampling and Surveying
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