How to Interpret R Output for Regression Analysis?

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As for determining if X and Y are significantly statistically related, the correlation coefficient of 0.93 indicates a strong positive linear correlation. However, you can also use other statistical tests to determine the significance of the relationship between X and Y.
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Ted123
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[PLAIN]http://img705.imageshack.us/img705/1016/20481574.jpg

Is there any way of reading the intercept and the slope of the least squares regression line from the R output here?

Also assuming the simple linear regression model [itex]Y=\beta_0+\beta_1 X + \varepsilon[/itex] where [itex]\varepsilon \stackrel {\text{i.i.d.}}{\sim} N(0,\sigma^2)[/itex] and [itex]X[/itex] are the ages of the buses and [itex]Y[/itex] the maintenance costs, how can I determine whether X and Y are significantly statistically related?

The correlation coefficient is [itex]r=0.93[/itex] which implies there is strong positive linear correlation between [itex]X[/itex] and [itex]Y[/itex] but does this mean they are significantly statistically related or can I use something else to determine this?
 
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For your first question, yes, you can read the slope and intercept from the data shown here. Your regression line equation is Y = 220.0 + 131.67X.
 

Related to How to Interpret R Output for Regression Analysis?

What is the purpose of interpreting R output?

The purpose of interpreting R output is to understand the results of statistical analyses and data transformations performed using the R programming language. It allows researchers and data analysts to draw meaningful conclusions from their data and make informed decisions based on the results.

What are the different types of R output?

There are several types of R output, including numerical summaries, graphs and plots, model diagnostics, and statistical tests. Numerical summaries provide descriptive statistics such as mean, median, and standard deviation. Graphs and plots visually represent the data, and model diagnostics assess the fit of statistical models. Statistical tests evaluate the significance of relationships between variables.

How do I interpret numerical summaries in R output?

Numerical summaries provide information about the central tendency, variability, and distribution of the data. The mean is the average value of the data, while the median is the middle value. The standard deviation measures the spread of the data from the mean. The distribution of the data can be inferred from the shape of the histogram or boxplot.

What do graphs and plots in R output tell us?

Graphs and plots in R output provide visual representations of the data, allowing us to identify patterns and trends. For example, a scatterplot can show the relationship between two variables, while a histogram can show the distribution of a single variable. Different types of graphs and plots are used to visualize different types of data and relationships.

How do I interpret statistical tests in R output?

Statistical tests in R output evaluate the significance of relationships between variables. The p-value indicates the probability of obtaining the observed results by chance, and a p-value less than 0.05 is typically considered statistically significant. It is also important to consider the effect size, which measures the strength of the relationship between variables.

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