Match the fallacy with how best to avoid it.

SAT

1 answer:

notka56 [123]2 years ago

5 0

Some types of fallacy are:

Argumentum ad Baculum
Argumentum ad Hominem
Straw man fallacy
The fallacy of hasty generalizations, etc

<h3>What is a Fallacy?</h3>

This refers to the mistaken belief about something that is based on an unsound argument.

Hence, we can see that your question is incomplete so I gave you a general overview to help you get a better understanding of the concept.

Read more about logical fallacies here:

brainly.com/question/4255659

#SPJ1

You might be interested in

Find the residual values, and use the graphing calculator tool to make a residual plot.

finlep [7]

Answer:

No, the points are evenly distributed about the x-axis.

Explanation:

1. Write the table with the data:

x given predicted residual

1 - 3.5 - 1.1

2 - 2.9 2

3 - 1.1 5.1

4 2.2 8.2

5 3.4 1.3

2. Complete the column of residuals

The residual is the observed (given) value - the predicted value.

residual = given - predicted.

Thus, the complete table, with the residual values are:

x given predicted residual

1 - 3.5 - 1.1 - 2.4

2 - 2.9 2 - 4.9

3 - 1.1 5.1 - 6.2

4 2.2 8.2 - 6.0

5 3.4 1.3 2.1

3. Residual plot

You must plot the last column:

x residual

1 - 2.4

2 - 4.9

3 - 6.2

4 - 6.0

5 2.1

See the plot attached.

Does the residual plot show that the line of best fit is appropriate for the data?

Ideally, a residual plot for a line of best fit that is appropiate for the data must not show any pattern; the points should be randomly distributed about the x-axis.

But the points of the plot are not randomly distributed about the x-axis: there are 4 points below the x-axis and 1 point over the x-axis: there are more negative residuals than positive residuals. This is a pattern. Also, you could say that they show a curve pattern, which drives to the same conclusion: the residual plot shows that the line of best fit is not appropiate for the data.

Thus, the conclusion should be: No, the points have a pattern.

1. "Yes, the points have no pattern": false, because as shown, the points do have a pattern, which makes the residual plots does not show that the line of best fit is appropiate for the data.

2. "No, the points are evenly distributed about the x-axis": true. As already said the points have a pattern. It is a curved pattern, and this shows the line of best fit is not appropiate for the data.

3. "No, the points are in a linear pattern": false. The points are not in a linear pattern.

4. "Yes, the points are in a curved pattern": false. Because the points are in a curved pattern, the residual plot shows that the line of best fit is not appropiate for the data.