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## 50%

#### 中級

You should know the basics of types of variables, distributions, hypothesis testing, p values and confidence intervals using R, though I'll recap.

### 您將學到的內容有

• Describe when a linear regression model is appropriate to use

• Read in and check a data set's variables using the software R prior to undertaking a model analysis

• Fit a multiple linear regression model with interactions, check model assumptions and interpret the output

### 您將獲得的技能

Correlation And DependenceLinear RegressionR Programming

## 50%

#### 中級

You should know the basics of types of variables, distributions, hypothesis testing, p values and confidence intervals using R, though I'll recap.

1

## INTRODUCTION TO LINEAR REGRESSION

7 個視頻 （總計 34 分鐘）, 9 個閱讀材料, 5 個測驗
7 個視頻
Pearson’s Correlation Part I3分鐘
Pearson’s Correlation Part II6分鐘
Intro to Linear Regression: Part I4分鐘
Intro to Linear Regression: Part II3分鐘
Linear Regression and Model Assumptions: Part I6分鐘
Linear Regression and Model Assumptions: Part II5分鐘
9 個閱讀材料
About Imperial College London & the Team10分鐘
How to be successful in this course10分鐘
Data set and Glossary10分鐘
Linear Regression Models: Behind the Headlines5分鐘
Linear Regression Models: Behind the Headlines: Written Summary20分鐘
Warnings and precautions for Pearson's correlation20分鐘
Introduction to Spearman correlation15分鐘
5 個練習
Linear Regression Models: Behind the Headlines10分鐘
Correlations30分鐘
Spearman Correlation20分鐘
Practice Quiz on Linear Regression20分鐘
End of Week Quiz20分鐘
2

## Linear Regression in R

3 個視頻 （總計 11 分鐘）, 8 個閱讀材料, 2 個測驗
3 個視頻
Fitting the linear regression3分鐘
Multiple Regression4分鐘
8 個閱讀材料
Recap on installing R10分鐘
Assessing distributions and calculating the correlation coefficient in R 10分鐘
Feedback10分鐘
How to fit a regression model in R10分鐘
Feedback15分鐘
Fitting the Multiple Regression in R30分鐘
Feedback10分鐘
Summarising correlation and linear regression30分鐘
2 個練習
Linear Regression20分鐘
End of Week Quiz20分鐘
3

## Multiple Regression and Interaction

4 個視頻 （總計 17 分鐘）, 9 個閱讀材料, 2 個測驗
4 個視頻
Introduction to Key Dataset Features: Part II2分鐘
Interactions between binary variables4分鐘
Interactions between binary and continuous variables5分鐘
9 個閱讀材料
How to assess key features of a dataset in R20分鐘
How to check your data in R10分鐘
Good Practice Steps20分鐘
Practice with R: Run a Good Practice Analysis30分鐘
Practice with R: Run Multiple Regression30分鐘
Feedback10分鐘
Practice with R: Running and interpreting a multiple regression30分鐘
Feedback15分鐘
2 個練習
Fitting and interpreting model results20分鐘
Interpretation of interactions20分鐘
4

## MODEL BUILDING

5 個視頻 （總計 16 分鐘）, 7 個閱讀材料, 2 個測驗
5 個視頻
Variable Selection3分鐘
Developing a Model Building Strategy6分鐘
Summary of developing a Model Building Strategy56
Summary of Course1分鐘
7 個閱讀材料
Feedback10分鐘
Further details of limitations of stepwise10分鐘
How many predictors can I include?10分鐘
Practice with R: Developing your model
Practice with R: Fitting the final model10分鐘
Feedback on developing the model10分鐘
Final R Code20分鐘
2 個練習
Problems with automated approaches20分鐘
End of Course Quiz20分鐘

## 關於 使用 R 进行公共健康领域的统计分析 專項課程

Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health. In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around. This specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019. The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data....

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