data <- babynames %>%
  filter(name == "Jordan") %>%
  group_by(year, sex) %>%
  summarize(count = sum(n), .groups = 'drop')

# Plot
plot_ly(data, x = ~year, 
        y = ~count, 
        color = ~sex, 
        type = 'scatter', 
        mode = 'markers', 
        text = ~sex) %>%
  
  layout(title = 'Name "Jordan" Popularity Correlation by Gender',
         xaxis = list(title = 'Year'),
         yaxis = list(title = 'Count'))

The formula for a simple linear regression model is:

\[ y = \beta_0 + \beta_1x + \epsilon \]

• \(y\) is the dependent variable.
• \(x\) is the independent variable.
• \(\beta_0\) is the y-intercept.
• \(\beta_1\) is the slope of the line.
• \(\epsilon\) is the error term.

This model attempts to describe the relationship between two variables by fitting a linear equation to observed data.

Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. Hypothesis Testing is basically an assumption that we make about the population parameter.

Key terms:

• Null hypothesis (\(H_0\)): Assumes that no effect or no difference exists.
• Alternative hypothesis (\(H_a\)): Contrary to the null hypothesis, it assumes that some difference or effect exists.

\[ H_0: \mu = \mu_0 \] \[ H_a: \mu \neq \mu_0 \]

Where \(\mu\) is the population mean and \(\mu_0\) is a specific value of the population mean that we are testing for.