What Is The Sccatter Plotss

The slope of the linear fit of the data is 1.0049. What this tells me about the water is that it is increasing at a close to constant rate – while my results were not completely accurate because the slope of the line was not one it was fairly close to the target

22) Which statement is true of a regression line that is superimposed on the scatter plot?

I will analyze the data by looking for a trend in the amount of fish caught in the morning vs at noon.

In the following graph, the exponential function has a very similar trend to the trend of y=2x. It is important to note that the exponential function is actually y=ax where a > 0. Using y=1x would only produce a horizontal line. Therefore the next value used was y=2x. In order to make the function fit better with the points we make transformations that slightly change the function. The first step is to lower the line so that the line can become closer to the data points. With this data set it sloa stands to reason that there are negative numbers very much possible. It would work in the exponential equation 2x-1.

Also the graph shows a negative correlation with a curve of best fit. This shows the relationship is not Ohmic as it is not a straight line.

This investigation will look into 3 Data Sets that have an X and Y value of time and value of Jetski, the years go up in 0.5 meaning every 6 months a new value is set in the data set tables. An investigation will be done to concour what data set best suits the real world scenario of Jetski ownership. This will be proved in a number of different mathematical methods and strategies. Many types of graphs will be utilised throughout this experiment for maximal analysis of data sets for example scatter plots will be drawn using the given data. Modifications are made to these graphs in order to create predictions, Regression line equations will be created, used and analysed in this experiment. Regression line equations are trend lines on

According to the second graph, there is more than a 15% difference in change in mass between the 0.2M and 1M sucrose solutions. The solution that had 0.2M of sucrose concentration had an average of 7.48% change in mass, 5.61% less than the 0.4M solution. Now comparing the rest of the solutions, with the 0.4 solution having a 11.2% change and the 0.6M solution with a 14.2% change, the difference between the two is exactly 3%, the 0.6M and 0.8M solutions have a 7.63% difference and the last two, 0.8M and 1M solutions with a 0.11% difference. Looking at the second graph again, the correlation coefficient is extremely high and the reason might not be as high as it could’ve been could be due to the fact that there were possible outliers. This tells us that although the line of best fit does not align with the data points perfectly, the relationship between the independent and dependent variable is very correlated. It is plausible, as it could be told from the first graph, that there could have been outliers like in the 0.4M solution, as one of the data points, 4.53%, might have potentially pulled the average change in mass for 0.4M down by more than

Scatter diagram purpose is to show how much one variable is affected by another. These charts will be used to gather the data; determine the high and low values for each factor. Decide which factor will be plotted on which axis. Theorizing a cause and effect relationship put the suspected cause on the horizontal axis and the alleged effect on the vertical axis. Draw and label the axes clearly. Make the axes roughly the same length, creating a square plotting area. Label each axis with increasing values from left to right, and from bottom to top. Label each axis to match the full range of values for that factor. In other words, make the lowest numerical label slightly less than the lowest data value, and the highest name slightly greater than the highest value. The data should fill the whole plotting

Residual plots. Using Minitab, the histogram and the normal probability plot of the residuals are shown in chart 2. The histogram shows the majority of the residuals are in the middle and centered on the mean of zero, while the tails have a lower number of residuals, while in the normal probability plot, the majority of the dots are close to the

The scattergram is like a graph or a bar chart, having a x and y at right angles to one another. In this case, the x axis represents one variable, while the y axis represents the second variable. The points on the scattergram are obtained by taking related measurements of the two variables. They might be related by being taken at the same time, or by coming from the same research participant,

On the graph above you can see that both the quadratic and the line are both adequate representations of the data collected by gold medalists for the men’s high jumps in the Olympics. Both of these lines follow the plots made on the original graph and they don’t stray too far from those lines either. There only outlier for the quadratic seem to be the medalist from the 1948 Olympics because his height is far below the quadratic. There might be some problems with the exact position of where the quadratic is and where the line is because they were drawn by hand and not on the computer like the stat plots which could potentially cause problems for interpreting the data.

The 5, 7 and 9 point Savitsky-Golay (SG) Smoothing is plot using the whiskey data (See figure 1.4, 1.5 and 1.6).

As the graph reflects the increase in income (Y) increases in the demand of money. Very similar to the IS curve, the relation is captured by plotting the income (Y) on the X-axis and the interest rate on the Y-axis, this jointly relation that deprived from the market is called the LM curve.

Figure ~\ref{fig:sa1_lm} shows the basic diagnostic plots for the full linear model. The residuals vs. fitted plot is used to determine if the linear relationship between the predictors (parameters) and outcome factors are captured by the model. An accurate model will have a residuals vs. fitted plot with a horizontal line with residuals above and below it without forming a pattern or grouping, as seen in the top left plot. Moreover, the normal Q-Q plot, shown in the top right plot, is used to determine if the residuals are normally distributed. A good normal distribution will have a normal Q-Q plot which follows a straight line, as do our results. Furthermore, the Scale-Location plot shows if the residuals are spread equally along all ranges of the predictors. Ideally, the residuals should be randomly spread across all fitted values and not tend to one area, as ours do in the bottom left plot. Likewise, the Residuals vs Leverage plot determines how big of an impact outliers points have on the regression curve. Additionally, outliers away from the Cook’s distance will have a large

The refined equation fits the data much more closely at the beginning, yet around the Age = 13 mark it starts to deviate again. Looking at this graph, one can come to the conclusion that it will take more than one mathematical function to graph the entirety of the data.