Chapter 5, Problem 5.15P

a)

Interpretation Introduction

Interpretation:

The least squares parameters including ${\text{R}}^{\text{2}}$ and scatter chart between signal and concentration has to be plotted.

Concept Introduction:

LINEST:

The LINEST is a Microsoft excel function which is uses the least squares method to measure the statistics for a straight line and array describing that line it is built-in function that can be categorized.

LINEARITY:

It is measure of how well a calibration curve follows a straight line and displaying that the response is proportional to the quantity of the analyte.

${\text{R}}^{\text{2}}\text{=}\frac{{\left[{\displaystyle \sum \left({\text{x}}_{\text{i}}\text{-}\stackrel{\text{-}}{\text{x}}\right)}\left({\text{y}}_{\text{i}}\text{-}\stackrel{\text{-}}{\text{y}}\right)\right]}^{\text{2}}}{{\displaystyle \sum {\left({\text{x}}_{\text{i}}\text{-}\stackrel{\text{-}}{\text{x}}\right)}^{\text{2}}}{\displaystyle \sum {\left({\text{y}}_{\text{i}}\text{-}\stackrel{\text{-}}{\text{y}}\right)}^{\text{2}}}}$

Where R means square of correlation coefficient.

b)

Interpretation Introduction

Interpretation:

The Trendline has to be inserted and that Trendline and LINEST give identical results has to be verified.

Concept Introduction:

LINEST:

The LINEST is a Microsoft excel function which is uses the least squares method to measure the statistics for a straight line and array describing that line it is built-in function that can be categorized.

LINEARITY:

It is measure of how well a calibration curve follows a straight line and displaying that the response is proportional to the quantity of the analyte.

${\text{R}}^{\text{2}}\text{=}\frac{{\left[{\displaystyle \sum \left({\text{x}}_{\text{i}}\text{-}\stackrel{\text{-}}{\text{x}}\right)}\left({\text{y}}_{\text{i}}\text{-}\stackrel{\text{-}}{\text{y}}\right)\right]}^{\text{2}}}{{\displaystyle \sum {\left({\text{x}}_{\text{i}}\text{-}\stackrel{\text{-}}{\text{x}}\right)}^{\text{2}}}{\displaystyle \sum {\left({\text{y}}_{\text{i}}\text{-}\stackrel{\text{-}}{\text{y}}\right)}^{\text{2}}}}$

Where R means square of correlation coefficient.

c)

Interpretation Introduction

Interpretation:

The $\text{95\%}$ confidence interval y errors for the following bars has to be added and $\text{t}$ with the statement $\text{"=TINV}\left(\text{0}\text{.05,9}\right)\text{''}$ has to be calculated.