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Introduction to using Beamer for Presentations You can add a subtitle Ulrike Genschel Department of Statistics, Iowa State University January 24, 2014 U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 1/1

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Introduction to using Beamer for PresentationsYou can add a subtitle

Ulrike Genschel

Department of Statistics, Iowa State University

January 24, 2014

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 1 / 1

Sampling Distributions and the Central Limit Theorem

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

A small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 2 / 1

The previous slide somewhat fancier...Sampling Distribution of the sample mean

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

A small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 3 / 1

The previous slide somewhat fancier...Sampling Distribution of the sample mean

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

A small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 3 / 1

The previous slide somewhat fancier...Sampling Distribution of the sample mean

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

A small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 3 / 1

The previous slide somewhat fancier...Sampling Distribution of the sample mean

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

A small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 3 / 1

The previous slide somewhat fancier...Sampling Distribution of the sample mean

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

A small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 3 / 1

The previous slide somewhat fancier...Sampling Distribution of the sample mean

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

A small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 3 / 1

The previous slide somewhat fancier...Sampling Distribution of the sample mean

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

a small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 4 / 1

The previous slide somewhat fancier...Sampling Distribution of the sample mean

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

a small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 4 / 1

The previous slide somewhat fancier...Sampling Distribution of the sample mean

Describing the sampling distribution of the sample mean x̄ :

mean of the sampling distribution (µx̄)

spread of the sampling distribution (σx̄)

shape of the sampling distribution

a small formula

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 4 / 1

Uncovering text

To uncover text you can use the command

\setbeamercovered{transparent}

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 5 / 1

Theorems and such

Definition

A triangle that has a right angle is called a right triangle.

Theorem

In a right triangle, the square of hypotenuse equals the sum of squares oftwo other sides.

Proof.

We leave the proof as an exercise to our astute reader. We also suggestthat the reader generalize the proof to non-Euclidean geometries.

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 6 / 1

Theorems and such

Definition

A triangle that has a right angle is called a right triangle.

Theorem

In a right triangle, the square of hypotenuse equals the sum of squares oftwo other sides.

Proof.

We leave the proof as an exercise to our astute reader. We also suggestthat the reader generalize the proof to non-Euclidean geometries.

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 6 / 1

Theorems and such

Definition

A triangle that has a right angle is called a right triangle.

Theorem

In a right triangle, the square of hypotenuse equals the sum of squares oftwo other sides.

Proof.

We leave the proof as an exercise to our astute reader. We also suggestthat the reader generalize the proof to non-Euclidean geometries.

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 6 / 1

Theorems and such

Definition

A triangle that has a right angle is called a right triangle.

Theorem

In a right triangle, the square of hypotenuse equals the sum of squares oftwo other sides.

Proof.

We leave the proof as an exercise to our astute reader. We also suggestthat the reader generalize the proof to non-Euclidean geometries.

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 6 / 1

Adding Boxes & using enumerated

Describing the sampling distribution of the sample mean x̄ :

1 mean of the sampling distribution (µx̄)

2 spread of the sampling distribution (σx̄)

3 shape of the sampling distribution

Central Limit Theorem (CLT)

If we draw a simple random sample of size n from any population withmean µ and standard deviation σ and n is sufficiently large, then thesampling distribution of the sample mean x̄ is approximately normal:

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 7 / 1

Adding Boxes & using enumerated

Describing the sampling distribution of the sample mean x̄ :

1 mean of the sampling distribution (µx̄)

2 spread of the sampling distribution (σx̄)

3 shape of the sampling distribution

Central Limit Theorem (CLT)

If we draw a simple random sample of size n from any population withmean µ and standard deviation σ and n is sufficiently large, then thesampling distribution of the sample mean x̄ is approximately normal:

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 7 / 1

Adding Boxes & using enumerated

Describing the sampling distribution of the sample mean x̄ :

1 mean of the sampling distribution (µx̄)

2 spread of the sampling distribution (σx̄)

3 shape of the sampling distribution

Central Limit Theorem (CLT)

If we draw a simple random sample of size n from any population withmean µ and standard deviation σ and n is sufficiently large, then thesampling distribution of the sample mean x̄ is approximately normal:

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 7 / 1

Adding Boxes & using enumerated

Describing the sampling distribution of the sample mean x̄ :

1 mean of the sampling distribution (µx̄)

2 spread of the sampling distribution (σx̄)

3 shape of the sampling distribution

Central Limit Theorem (CLT)

If we draw a simple random sample of size n from any population withmean µ and standard deviation σ and n is sufficiently large, then thesampling distribution of the sample mean x̄ is approximately normal:

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 7 / 1

Adding Boxes & using enumerated

Describing the sampling distribution of the sample mean x̄ :

1 mean of the sampling distribution (µx̄)

2 spread of the sampling distribution (σx̄)

3 shape of the sampling distribution

Central Limit Theorem (CLT)

If we draw a simple random sample of size n from any population withmean µ and standard deviation σ and n is sufficiently large, then thesampling distribution of the sample mean x̄ is approximately normal:

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 7 / 1

Adding Boxes & using enumerated

Describing the sampling distribution of the sample mean x̄ :

1 mean of the sampling distribution (µx̄)

2 spread of the sampling distribution (σx̄)

3 shape of the sampling distribution

Central Limit Theorem (CLT)

If we draw a simple random sample of size n from any population withmean µ and standard deviation σ and n is sufficiently large, then thesampling distribution of the sample mean x̄ is approximately normal:

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 7 / 1

Adding Boxes & using enumerated

Describing the sampling distribution of the sample mean x̄ :

1 mean of the sampling distribution (µx̄)

2 spread of the sampling distribution (σx̄)

3 shape of the sampling distribution

Central Limit Theorem (CLT)

If we draw a simple random sample of size n from any population withmean µ and standard deviation σ and n is sufficiently large, then thesampling distribution of the sample mean x̄ is approximately normal:

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 7 / 1

Adding Boxes & using enumerated

Describing the sampling distribution of the sample mean x̄ :

1 mean of the sampling distribution (µx̄)

2 spread of the sampling distribution (σx̄)

3 shape of the sampling distribution

Central Limit Theorem (CLT)

If we draw a simple random sample of size n from any population withmean µ and standard deviation σ and n is sufficiently large, then thesampling distribution of the sample mean x̄ is approximately normal:

x̄ approximately N(µ,

σ√n

)

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 7 / 1

Changing Itemization Markers

Markers can be changed using the command

\setbeamertemplate{items}[]

\setbeamertemplate{items}[circle]

\setbeamertemplate{items}[ball]

\setbeamertemplate{items}[rectangle]

\setbeamertemplate{items}[triangle]

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 8 / 1

Changing Itemization Markers

Markers can be changed using the command

\setbeamertemplate{items}[]

\setbeamertemplate{items}[circle]

\setbeamertemplate{items}[ball]

\setbeamertemplate{items}[rectangle]

\setbeamertemplate{items}[triangle]

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 8 / 1

Changing Itemization Markers

Markers can be changed using the command

\setbeamertemplate{items}[]

\setbeamertemplate{items}[circle]

\setbeamertemplate{items}[ball]

\setbeamertemplate{items}[rectangle]

\setbeamertemplate{items}[triangle]

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 8 / 1

Changing Itemization Markers

Markers can be changed using the command

\setbeamertemplate{items}[]

\setbeamertemplate{items}[circle]

\setbeamertemplate{items}[ball]

\setbeamertemplate{items}[rectangle]

\setbeamertemplate{items}[triangle]

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 8 / 1

Including graphs/pictures

The graphics package supports the most common graphic formats .pdf,.jpg, .jpeg, and .png. Other formats must be converted to a supportedformat in an external editor.

rule.pdf

Figure : This is my figure 1.

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 9 / 1

Splitting a slide into columns

The line you are reading goes all the way across the slide. From the leftmargin to the right margin. Now we are going the split the slide into twocolumns.

Here is the first column. We put anitemized list in it.

This is an item

This is another item

Yet another item

Here is the secondcolumn. We will put apicture in it.

rule.pdf

Figure : Figure 2

The line you are reading goes all the way across the slide. From the leftmargin to the right margin.

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 10 / 1

Splitting a slide into columns

The line you are reading goes all the way across the slide. From the leftmargin to the right margin. Now we are going the split the slide into twocolumns.

Here is the first column. We put anitemized list in it.

This is an item

This is another item

Yet another item

Here is the secondcolumn. We will put apicture in it.

rule.pdf

The line you are reading goes all the way across the slide. From the leftmargin to the right margin.

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 11 / 1

The default font size

Beamer’s default font size is 11 points. It is possible to set the defaultfont size to any of 8, 9, 10, 11, 12, 14, 17, 20 in the

\documentclass

For instance, to set the default font to 14 points, do:

\documentclass[14pt]{beamer}

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 12 / 1

Changing font sizes throughout the presentation

mean of the sampling distribution

the mean of the sampling distribution of the sample mean is equal tothe population mean

µx̄ = µ

spread of the sampling distribution

the spread of the sampling distribution of the sample mean is equal tothe population standard deviation divided by square root of the samplesize

σx̄ = σ√n

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 13 / 1

Changing font sizes throughout the presentation

mean of the sampling distributionmean of the sampling distributionmean of the sampling distribution

mean of the sampling distributionmean of the sampling distribution

mean of the sampling distributionmean of the sampling distribution

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 14 / 1

Changing colors

We can change

foreground color (fg)

\setbeamercolor{normal text}{fg=purple}

background color (bg)

\setbeamercolor{normal text}{bg=blue!12}

\setbeamertemplate{background canvas}[vertical shading]

[bottom=red!20,top=yellow!30]

overall color theme of the presentation

\usecolortheme[named=Red]{structure}

Note, many variations of colors are possible — pick whatever you like

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 15 / 1

Including Page numbers manually

To include page numbers manually (for plain beamer themes e.g. defaultor boxes) use following commands

\usepackage{fancyhdr,lastpage}

\pagestyle{fancy}\fancyhf{}\rfoot{\vspace{-0.5cm} Page

{\thepage} of \pageref{LastPage}}

Need to download following style files fancyhdr.sty and lastpage.sty

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 16 / 1

Useful resources

http://www.math.umbc.edu/∼rouben/beamer/quickstart.html

http://latex-beamer.sourceforge.net/

to download beamer

beamer user guide

U. Genschel (Dep. of Statistics, ISU) Intro Beamer January 24, 2014 17 / 1