Step 1

Remember that regression analysis is the process of looking for a best fit of model for a set of data. This can be done on a graphing utility as follows:

1. Press [STAT], the input corresponging x-values of data in L1, and y-values of data in L2.

2. Use [STATPLOT] to observe a scatterplot of the data.

3. Press [STAT], then [CALC] then [ExpReg]/[LnReg]/[Logistic].

This will show you a function in either the form of an exponential, a logarithmic or a logistic model.

4. Graph this equation on the same window as the scatterplot to see if it fits the data.

Step 2

1. Press [STAT], the input corresponging x-values of data in L1, and y-values of data in L2.

2. Use [STATPLOT] to observe a scatterplot of the data.

Step 3

Based on the plots of the points, it can be exponential or logarithmic.

However, upon checking both regression analysis, the one with the closest value of \(\displaystyle{r}^{{{2}}}\) to 1 is exponential, hence, its formula is \(\displaystyle{y}={628.67663}{\left({0.64841}\right)}^{{{x}}}.\) The graph of which is below:

Remember that regression analysis is the process of looking for a best fit of model for a set of data. This can be done on a graphing utility as follows:

1. Press [STAT], the input corresponging x-values of data in L1, and y-values of data in L2.

2. Use [STATPLOT] to observe a scatterplot of the data.

3. Press [STAT], then [CALC] then [ExpReg]/[LnReg]/[Logistic].

This will show you a function in either the form of an exponential, a logarithmic or a logistic model.

4. Graph this equation on the same window as the scatterplot to see if it fits the data.

Step 2

1. Press [STAT], the input corresponging x-values of data in L1, and y-values of data in L2.

2. Use [STATPLOT] to observe a scatterplot of the data.

Step 3

Based on the plots of the points, it can be exponential or logarithmic.

However, upon checking both regression analysis, the one with the closest value of \(\displaystyle{r}^{{{2}}}\) to 1 is exponential, hence, its formula is \(\displaystyle{y}={628.67663}{\left({0.64841}\right)}^{{{x}}}.\) The graph of which is below: