Line Of Regression given x? You have found the regression line for a set of data points to be: ŷ = 30.23x + 173.52. Use the line to predict the value of y when x = 48.
Plug $x=48$ into the original equation:
$$\hat y = 30.23(48)+173.52$$
$$\hat y = 1624.56$$
Here's a quick overview of what you mean, by regressing $y$ on $x$. 

Linear regression essentially tries to establish a straight-line relationship between a variable of interest $y$ and a factor $x$ (sometimes multiple factors $x_{1},x_{2},\cdots,x_{k}$) that is(are) believed to explain(cause) $y$. 

Linear regression assumes that $y$ is a linear function of $x$. Any $y_{i}$ can be computed, if $x_{i}$ is known.

$y_{i}=a+bx_{i}$ 

As an example, you believe that the average returns on Google's stock trading on the stock exchange has a significant relationship to the average returns on the S&amp;P 500 stock index. 

Let's denote,

The expected annual returns on Google's stock by $E(R_{google})$.

The expected annual returns on the S&amp;P500 index by $E(R_{market})$.

You would then study the historical data of Google's stock returns, say over the last 25 years and likewise the S&amp;P 500 index returns for the past 25 years. In reality, you are collecting a sample.

Since we are performing linear regression, we assume that $E(R_{google})$ is a linear function of $E(R_{market})$. 

$E(R_{google})=a+\beta\cdot{E(R_{market})}$

Using the sample data, mathematicians then use a method like Ordinary Least Squares(OLS) to estimate the coefficients $a$ and $\beta$. You would then arrive at an equation like (I just mocked it up),

$E(R_{google})=4+(1.2){E(R_{market})}$

or like the one you gave us,

$\hat{y}=30.23x+173.52$

Graphically, this is equivalent of plotting the sample data - the set of $(x_{i},y_{i})$'s with $E(R_{market})$ on the X-axis, and $E(R_{google})$ on Y-axis and passing a straight line through these points which is the best fit.

Once you've built a regression model, you could predict the dependent variable $y$ for any value of the independent variable $x$. You forecast $y$ by simply plugging in the x-value in the regression equation.

Regression is used in various disciplines to study different phenomena e.g. man-power planning a company, predicting inflation in an economy, predicting the long-run exchange rates, predicting the volatility of option prices, predicting the energy demands given the temperature and day of the year etc.