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Beamer overbox full error and image not inserting

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}

One Answer

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.

enter image description here

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:

enter image description here

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}

Answered by Sebastiano on January 5, 2022

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