Summarize the following:
You have probably heard the famous saying that “breakfast is the most important meal of the day,” and it’s true! That is because when you wake up, you likely haven’t eaten for 8 to 10 hours. Your blood sugar is low, and so are your energy levels. You need to “break” the “fast” within one hour of waking up. A healthy breakfast will jump start your metabolism and your day, but waiting too long to eat can actually slow you down. Typical breakfast choices (such as a bagel with cream cheese or toast with jam) can give you some quick energy, but they will spike your blood sugar and leave you to crash. This can lead to insulin resistance, plus it will leave you feeling hungry again soon. Stay away from white bread and sweets in the morning.  You'll want to stay away from donuts, waffles, pancakes, and white toast. Opt for 100% whole wheat toast instead. Watch out for added sugar in oatmeal or breakfast cereals. Refrain from adding sugar (or sweetened creamer) to your coffee. To really get your metabolism started in the morning, you must also include a serving of healthy fats. This can be as simple as adding a teaspoon of coconut oil to your brown rice porridge, cooking your omelet in clarified butter, or enjoying a spoonful of peanut butter. Add some healthy fat to your meal to rev your metabolism and feel full. However, keep in mind that there may already be fat in your breakfast from things like yogurt, milk, and breakfast sausage. Not only does breakfast help jump start your metabolism and help give you energy all day, but the heartier your breakfast is, the better! A recent study shows that getting 22 to 55% of your total calories at breakfast may help you stay slim, whereas getting 0 to 11% of your calories from breakfast may lead to weight gain. So fill up your plate and get full at breakfast.

summary: Eat breakfast within one hour of waking. Avoid simple carbohydrates. Eat healthy fats. Fill up your plate.


Summarize the following:
In the fourth column of your data table, you will calculate and record the error of each predicted value. Specifically, subtract the predicted value (y′{\displaystyle y^{\prime }}) from the actual observed value (y{\displaystyle y}). For the data in the sample set, these calculations are as follows:  y(x)−y′(x){\displaystyle y(x)-y^{\prime }(x)} y(1)−y′(1)=2−2.8=−0.8{\displaystyle y(1)-y^{\prime }(1)=2-2.8=-0.8} y(2)−y′(2)=4−3.4=0.6{\displaystyle y(2)-y^{\prime }(2)=4-3.4=0.6} y(3)−y′(3)=5−4=1{\displaystyle y(3)-y^{\prime }(3)=5-4=1} y(4)−y′(4)=4−4.6=−0.6{\displaystyle y(4)-y^{\prime }(4)=4-4.6=-0.6} y(5)−y′(5)=5−5.2=−0.2{\displaystyle y(5)-y^{\prime }(5)=5-5.2=-0.2} Take each value in the fourth column and square it by multiplying it by itself. Fill in these results in the final column of your data table. For the sample data set, these calculations are as follows:  −0.82=0.64{\displaystyle -0.8^{2}=0.64} 0.62=0.36{\displaystyle 0.6^{2}=0.36} 12=1.0{\displaystyle 1^{2}=1.0} −0.6=0.36{\displaystyle -0.6=0.36} −0.2=0.04{\displaystyle -0.2=0.04} The statistical value known as the sum of squared errors (SSE) is a useful step in finding standard deviation, variance and other measurements. To find the SSE from your data table, add the values in the fifth column of your data table. For this sample data set, this calculation is as follows: 0.64+0.36+1.0+0.36+0.04=2.4{\displaystyle 0.64+0.36+1.0+0.36+0.04=2.4} The Standard Error of the Estimate is the square root of the average of the SSE. It is generally represented with the Greek letter σ{\displaystyle \sigma }. Therefore, the first calculation is to divide the SSE score by the number of measured data points. Then, find the square root of that result.  If the measured data represents an entire population, then you will find the average by dividing by N, the number of data points. However, if you are working with a smaller sample set of the population, then substitute N-2 in the denominator. For the sample data set in this article, we can assume that it is a sample set and not a population, just because there are only 5 data values. Therefore, calculate the Standard Error of the Estimate as follows:  σ=2.45−2{\displaystyle \sigma ={\sqrt {\frac {2.4}{5-2}}}} σ=2.43{\displaystyle \sigma ={\sqrt {\frac {2.4}{3}}}} σ=0.8{\displaystyle \sigma ={\sqrt {0.8}}} σ=0.894{\displaystyle \sigma =0.894} The Standard Error of the Estimate is a statistical figure that tells you how well your measured data relates to a theoretical straight line, the line of regression. A score of 0 would mean a perfect match, that every measured data point fell directly on the line. Widely scattered data will have a much higher score. With this small sample set, the standard error score of 0.894 is quite low and represents well organized data results.
summary: Calculate the error of each predicted value. Calculate the squares of the errors. Find the sum of the squared errors (SSE). Finalize your calculations. Interpret your result.