Slopes

By determining if 0 is contained in the interval

By computing the R-squared value

By finding the p-value

By comparing the slope to the mean value

The standard error of the slope

The width of the confidence interval

The number of confidence intervals constructed

The percentage of confidence intervals that would contain the true value

Confidence level

Margin of error

Probability value

Sampling variability

The most likely outcome is that there is no statistically significant linear trend since the interval includes 0.

There could be either positive or negative linear trend but no more than 5 nor less than -1 unit change in response variable per unit change in X-variable.

This indicates a strong positive trend with predicted increase of exactly five units in Y-response to every additional X-unit.

The slope is too small to note any practical significance hence no need to consider it in statistical modeling.

Judgemental sampling

Simple random sampling (SRS)

Quota sampling

Snowball sampling

b^3

b^2

(b-1)

b

There is not enough evidence to conclude that there is a significant relationship.

The variability in the data does not affect the slope estimate.

There is conclusive evidence of a strong positive relationship.

Zero falls outside the range of plausible values for the population slope.

Purposive sampling

Non-probability sampling

Voluntary response sampling

Convenience sampling

P-value

Standard deviation

Confidence interval

Z-score

Because it assists in confirming normal distribution of residuals, thus ensuring model validity for applications in real-world scenarios.

Because it is required by the statistical community to prevent unethical manipulation of results presented publicly.

Because not including zero violates statistical assumptions required for proper regression analysis.

Because including zero allows us to determine whether there exists significance between predictor and response beyond just due chance.