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The birth of the first child is a special event for a mother whose life can change dramatically. In Ethiopia women’s timing to enter motherhood vary between the regions. This paper is therefore focusing on how birth cohort, education and residence affect the rate of entering motherhood for Ethiopian women in the different regions and the entire country. The dataset is extracted from the 2016 Ethiopia Demographic and Health Survey (EDHS) and contains 15,019 women from 487 different households. For more accurate estimations and results, the correlation within households is taken into consideration with multilevel survival analysis. The methods used are the Cox proportional hazard model and two frailty models. The results of the paper show that women residing in rural areas have an increased rate of entering motherhood compared to those residing in urban areas, every age group older than those born 1997 to 2001 have a higher intensity to enter parenthood and those with education have a decreased intensity ratio compared to the women with no education. It also shows that there is a regional difference in the effect of the estimated ratios of the covariates. Performing the multilevel analysis only changes the estimated effects of the covariates in the cities and one region. It is concluded that the estimated intensity ratio of multilevel survival analysis only varies from the standard Cox regression when the region is heterogeneous.

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In this thesis, we examine the machine learning models logistic regression, multilayer perceptron and random forests in the purpose of discriminate between good and bad credit applicants. In addition to these models we address the problem of imbalanced data with the Synthetic Minority Over-Sampling Technique (SMOTE). The data available have 273 286 entries and contains information about the invoice of the applicant and the credit decision process as well as information about the applicant. The data was collected during the period 2015-2017. With AUC-values at about 73%some patterns are found that can discriminate between customers that are likely to pay their invoice and customers that are not. However, the more advanced models only performed slightly better than the logistic regression.

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In this thesis, the inverse distance weighting, different kriging methods, ordinary least squares and two variants of the geographically weighted regression was used to evaluate the spatial mapping abilities on an observed dataset and a simulated dataset. The two datasets contain the same bioclimatic variable, near-surface air temperature, uniformly distributed over the whole world. The observed dataset is the observed temperature of a global atmospheric reanalysis produced by ECMWF and the other being simulated temperature produced by SMHI’s climate model EC-earth 3.1. The data, initially containing space-time information during the time period 1993-2010 displayed no significant temporal variation when using a spatio-temporal variogram. However, each year displayed its own variation so each year was split where the different methods were used on the observed dataset to estimate a surface for each year that was then used to make comparisons to the simulated data. CLARA clustering was done on the observed geographical dataset in the hope to force the inverse distance weighting and the kriging methods to estimate a locally varying mean. However, the variograms produced displayed an irregular trend that would lead to inaccurate kriging weights. Geometric anisotropy variogram analysis was accounted for that displayed moderate anisotropy. Results show that the geographically weighted regression family outperformed the rest of the used methods in terms of root mean squared error, mean absolute error and bias. It was able to create a surface that had a high resemblance to the observed data.