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Introduction to Applied Statistics

Introduction to Applied Statistics

Introduction to Applied Statistics. Xiaobo Sheng. Overview. CH 1 Introduction CH 2-3 Concepts, Descriptive Statistics of one variable CH 6-8 Probability, A few common probability distributions and models CH 9-13 Statistical Inference CH 15 Linear Regression.

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3.13k views • 146 slides
Lecture 2

Lecture 2

Lecture 2. Themes in this session Knowledge discovery in databases Data mining Multidimensional analysis and OLAP. Knowledge discovery in databases. What is Knowledge?. Data symbols representing properties of events and their environments Information

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650 views • 42 slides
Simple Linear Regression

Simple Linear Regression

Simple Linear Regression. Relationship Between Two Quantitative Variables. If we can model the relationship between two quantitative variables, we can use one variable, X, to predict another variable, Y. Use height to predict weight.

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501 views • 40 slides
Part IV The General Linear Model Multiple Explanatory Variables Chapter 13.4 Fixed * Random Effects Randomized block

Part IV The General Linear Model Multiple Explanatory Variables Chapter 13.4 Fixed * Random Effects Randomized block

Part IV The General Linear Model Multiple Explanatory Variables Chapter 13.4 Fixed * Random Effects Randomized block.

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453 views • 36 slides
Linear Automobile Depreciation

Linear Automobile Depreciation

Linear Automobile Depreciation. Section 5-5. Linear Depreciation. Most cars will not be worth their purchase price as they get older They depreciate in value over time Lose value Some collectible cars increase in value over time Appreciate. Linear Depreciation.

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666 views • 34 slides
Linear Automobile Depreciation

Linear Automobile Depreciation

Linear Automobile Depreciation. Section 5-5. Linear Depreciation. Most cars will not be worth their purchase price as they get older They depreciate in value over time Lose value Some collectible cars increase in value over time Appreciate. Linear Depreciation.

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546 views • 34 slides
Part IV The General Linear Model. Multiple Explanatory Variables Chapter 13.3 Fixed *Random Effects Paired t-test

Part IV The General Linear Model. Multiple Explanatory Variables Chapter 13.3 Fixed *Random Effects Paired t-test

Part IV The General Linear Model. Multiple Explanatory Variables Chapter 13.3 Fixed *Random Effects Paired t-test. Overview of GLM. Simple regression Multiple regression. Regression. ANOVA. Two categories (t-test) Multiple categories - Fixed (e.g., treatment, age)

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429 views • 32 slides
Beyond The Uncertainty:

Beyond The Uncertainty:

Beyond The Uncertainty:. Strategies to Secure your Retirement Income for Life. Planning the Optimal Distribution Strategy for Retirement. Can I continue living in my accustomed comfort and enjoyment? Will I be able to leave an estate to my heirs?

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337 views • 25 slides
Introduction

Introduction

Part III The General Linear Model. Multiple Explanatory Variables Chapter 12 Multiple Regression. Introduction. GLM, one explanatory: Regression: t-test/ANOVA: GLM, multiple explanatory: Multiple Regression Multi-way ANOVA ANCOVA .

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341 views • 20 slides
Regression Statistics

Regression Statistics

Regression Statistics. Engineers and Regression. Engineers often: Regress data to a model Used for assessing theory Used for predicting Empirical or theoretical model Use the regression of others Antoine Equation DIPPR. What are uncertainties of regression ? Do the data fit the model?

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299 views • 15 slides
Introduction to Regression Analysis

Introduction to Regression Analysis

Introduction to Regression Analysis. Dependent variable (response variable). Measures an outcome of a study Income GRE scores Dependent variable = Mean (expected value) + random error y = E(y) + ?

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180 views • 13 slides

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