The Regression Model Of The United States

In conclusion, logistic model is better fit for the data than exponential model. They both describe the increasing tendency of the increase rate at first several trails. But only logistic model describes the decreasing tendency of the increase rate at the

The study and application of macroeconomics influences the well-being of a nation by achieving high rates of material production and by keeping track of how much of something is being consumed. The United States is one of the wealthiest countries in the globe, making the government powerful. Government intervention in the Untied States is an important factor that keeps the economy running. Enough power to control the business cycle keeps money circulating the nation. The business cycle includes economic downturns, classified as recessions, expansions, business-cycle peaks and troughs. A good government is essential for the economy to run smoothly. There are three main macroeconomic variables in the nation that the government focuses on, Gross Domestic Product (GDP), unemployment rate, and inflation rate.

|What criterion must be met |Consistency: Important when comparing data to make sure the data compared was prepared the correct way and done the same each time. |

Those three types of tests were combined to make new tests. But the results are all similar to the ones mentioned before.

There are 50 credit customers who were selected for the data collection on five variables such as location, income, size, years, and credit balance. In order to understand more about their customer, AJ DAVIS must use graphical, numerical summary to be able to interpret and better expand their business in the future.

The training and test samples are selected based on the ground truth of the original image of AVIRIS and HYDICE data.

This algorithm was simulated with Matlab. These datasets and the mentioned characteristics are considered and the algorithm of each dataset with different slopes for the activation fumcion of interest were evaluated so that the best slope can be obtained. After running the program for several times and computing the average to obtain the best result, the optimum slope was evaluated for each dataset and the best slopes for Breast Cancer, Diabetes, Bupa, and

Instead we use the original predictors to predict the response. The original dataset was split into a training set that consists of 75% of the total observations and a test set that consists of 25% of the total observations. Observations were chosen randomly. Supervised learning methods was conducted on the training set to obtain a model, then the model was used on the test set to assess the prediction performance. The values for “K” in KNN were tuned via cross-validation. Due to the volume of the data, the “cost” parameter in the SVM was chosen somewhat ad hoc and the “mtry” parameter in the random forest was chosen as default. The error rates are as

These measurements include the assessment of risk factors[61], quality of care[62], diagnostic criteria[63], etc. Most of these studies used rule-based method[62, 63] to detect clearly defined and less complex (fewer expression variations) measurements, such as glucose level and body mass index. For some ambiguous and complex measurements, such as coronary artery disease and obesity status, machine learning plus external terminologies[61] are often

Latent class model (LCM) is gaining popularity in health care research. LCM has edge over other conventional modeling as it can incorporate one or more discrete unobserved variables. In addition, it does not depend on traditional assumptions (linear relationship, normal distribution, homogeneity). In their study Santos Silva and Windmeijer (2001) showed that hurdle model is unable to separately identify two decision processes. In health care utilization data, it is very hard to differentiate different illness spell during the one year period. The type of illness may affect both zero and positive outcomes, but, the zero-inflated models only take into account excess zeroes. Latent class models are able to capture this phenomena (Dev and Trivedi

The United States is currently experiencing a slow recovery from the recession of 2008-09. The current unemployment rate is 7.7%, which is the lowest level since December of 2008 (BLS, 2012). However, this rate is believed to higher than the rate that would occur if the economy was operating at peak efficiency, and it is also believed that there are structural issues still underpinning this performance. For example, the number of Americans who have exited the work force as the result of prolonged unemployment is believed to be higher than usual. In addition, the Congressional Budget Office (CBO, 2012) notes that long-term unemployment of greater than 26 weeks is at a much higher rate than normal, which will have adverse long-run effects on the economy, since workers with long-term unemployment often find their career paths derailed.

With the positive coefficients, we will see an increase in one unit of each variable separately compared with the advancement in diabetes. With a 0.05 parameter, the linear regression model selects 5 predictor variables with significance, age, tc, ldl, tch, and glu. To validate the assumption, we can plot the residuals versus the fitted values to see if there are any indications of signs of random distributions. For the residual plot, we see there are no indications or violations of random distribution and can calculate the MSE of the model, which is 3111.265. Next, we will leverage the best subset method to select the predictor variables that are truly impactful to the model.

Before a data set can be mined, it first has to be ?cleaned?. This cleaning process removes errors, ensures consistency and takes missing values into account. Next, computer algorithms are used to ?mine? the clean data looking for unusual patterns. Finally, the patterns are interpreted to produce new knowledge.3

This study utilized the Worchester Heart Attack Study data and R Studio software to predict the mortality factors for heart attack patients. The medical data include physiological measurements about heart attack patients, which serve as the independent variables, such as the heart rate, blood pressure, atria fibrillation, body mass index, cardiovascular history, and other medical signs. This study employed the techniques of supervised learning and unsupervised learning algorithms, using classification decision trees and k-means clustering, respectively. In addition to performing initial descriptive statistics to estimate the general range of critical factors correlated with heart attack patients, R Studio was used to determine the weight of each of the significant factors on the prediction in order to quantify its influence on the death of heart attack patients. Furthermore, the software was used to evaluate the accuracy of the predicted model to estimate death of heart attack patients by using a confusion matrix to compare predictions with actual data. Finally, this study reflected on the effectiveness of the data mining software conclusions, compared supervised learning and unsupervised learning, and conjectured improvements for future data mining investigations.

The objective function, decision variables and constraints are fed into solver to arrive at the optimal solution as shown in the below screenshot