TeX - LaTeX Asked on January 5, 2022

I’m trying to make beamer presentation, but getting an

1. overbox full error and also,

2. the image is not getting inserted.

3. Also, at the end of beamer, my name and institution name, both are getting reflected. I want only the institution name to appear below.

```
documentclass{beamer}
usetheme{Boadilla}
usepackage[utf8]{inputenc}
usepackage{amsmath}
usepackage{mathtools}
usepackage{booktabs}
usepackage{geometry}
usepackage{graphicx,wrapfig}
graphicspath{E:MSESem IVAlgorithmsproject}
usepackage{fourier}
usepackage{tabularx}
usepackage{tkz-graph}
usepackage{tikz}
usetikzlibrary{decorations.markings}
tikzstyle{vertex}=[circle, draw, inner sep=0pt, minimum size=6pt]
usepackage{array}
newcommand{vertex}{node[vertex]}
newcounter{Angle}
usepackage{tabto}
usepackage{makecell}
newcommandmyatop[2]{left[{{#1}atop#2}right]} % "wrapper macro"
newcolumntype{Z}{ >{centeringarraybackslash}X }
title{Regression Types}
author{Vaibbhav Devender Kalra}
institute{Madras School of Economics}
date{today}
begin{document}
begin{frame}
titlepage
end{frame}
section{Linear Regression}
subsection{Simple Linear Regression}
begin{frame}
frametitle{Index}
tableofcontents
end{frame}
begin{frame}
frametitle{Linear Regression}
underline{Definition:} In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.
end{frame}
begin{frame}{Simple Linear Regression}
A normal regression equation of the form of;
$Y = hat{alpha} + hat{beta}X + e$\
where Y is the dependent variable and X is the independent variable. e represents the error term and $alpha$ & $beta$ are the intercept and slope coefficients.\
or, $e = Y - hat{alpha} - hat{beta}X $
end{frame}
begin{frame}
frametitle{Pictures}
begin{figure}
includegraphics[scale=0.5]{latex1.png}
caption{lion!!}
end{figure}
<text>
end{frame}
end{document}
```

I have done some fixes of your code. I have removed `amsmath`

package because `mathtools`

is a robust package than to `amsmath`

; after I have deleted fourier package because the symbol `hat`

doesn't give me a correct visualization. Lastly, I have inserted a test image called `example-image-a`

.

```
documentclass{beamer}
usetheme{Boadilla}
usepackage[utf8]{inputenc}
usepackage{amssymb}
usepackage{mathtools}
usepackage{booktabs}
usepackage{geometry}
usepackage{graphicx,wrapfig}
graphicspath{E:MSESem IVAlgorithmsproject}
usepackage{tabularx}
usepackage{tkz-graph}
usepackage{tikz}
usetikzlibrary{decorations.markings}
tikzstyle{vertex}=[circle, draw, inner sep=0pt, minimum size=6pt]
usepackage{array}
newcommand{vertex}{node[vertex]}
newcounter{Angle}
usepackage{tabto}
usepackage{makecell}
newcommandmyatop[2]{left[{{#1}atop#2}right]} % "wrapper macro"
newcolumntype{Z}{ >{centeringarraybackslash}X }
title{Regression Types}
author{Vaibbhav Devender Kalra}
institute{Madras School of Economics}
date{today}
begin{document}
begin{frame}
titlepage
end{frame}
section{Linear Regression}
subsection{Simple Linear Regression}
begin{frame}
frametitle{Index}
tableofcontents
end{frame}
begin{frame}
frametitle{Linear Regression}
underline{Definition:} In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.
end{frame}
begin{frame}{Simple Linear Regression}
A normal regression equation of the form of;
$Y = hat{alpha} + hat{beta}X + e$\
where Y is the dependent variable and X is the independent variable. e represents the error term and $alpha$ & $beta$ are the intercept and slope coefficients.\
or, $e = Y - hat{alpha} - hat{beta}X $
end{frame}
begin{frame}
frametitle{Pictures}
begin{figure}
includegraphics[scale=0.5]{example-image-a}
caption{lion!!}
end{figure}
end{frame}
end{document}
```

**ADDENDUM:** Considering that it is not necessary to use the path for the correct compilation as rightly observed by, remembering that in this excellent answer that `institute`

also has an optional argument `[...]`

, you could adapt the long title of your institute as follows:

```
documentclass{beamer}
usetheme{Boadilla}
usepackage[utf8]{inputenc}
usepackage{amssymb}
usepackage{mathtools}
usepackage{booktabs}
usepackage{geometry}
usepackage{graphicx,wrapfig}
usepackage{tabularx}
usepackage{tkz-graph}
usepackage{tikz}
usetikzlibrary{decorations.markings}
tikzstyle{vertex}=[circle, draw, inner sep=0pt, minimum size=6pt]
usepackage{array}
newcommand{vertex}{node[vertex]}
newcounter{Angle}
usepackage{tabto}
usepackage{makecell}
newcommandmyatop[2]{left[{{#1}atop#2}right]} % "wrapper macro"
newcolumntype{Z}{ >{centeringarraybackslash}X }
title{Regression Types}
author{}
institute[MSE]{Madras School of Economics}
date{today}
begin{document}
begin{frame}
titlepage
end{frame}
section{Linear Regression}
subsection{Simple Linear Regression}
begin{frame}
frametitle{Index}
tableofcontents
end{frame}
begin{frame}
frametitle{Linear Regression}
underline{Definition:} In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.
end{frame}
begin{frame}{Simple Linear Regression}
A normal regression equation of the form of;
$Y = hat{alpha} + hat{beta}X + e$\
where Y is the dependent variable and X is the independent variable. e represents the error term and $alpha$ & $beta$ are the intercept and slope coefficients.\
or, $e = Y - hat{alpha} - hat{beta}X $
end{frame}
begin{frame}
frametitle{Pictures}
begin{figure}
includegraphics[scale=0.5]{example-image-a}
caption{lion!!}
end{figure}
end{frame}
end{document}
```

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