Norstat recurrently performs a survey that contains questions about how much the respondent is watching different tv-channels, how different media-devices are used, the ownership of different devices and the usage of different tv-channel sites on the internet, social media, internet services, magazine services and streaming services. In this thesis, data from the survey performed during the autumn of 2016 was used. The aim of this thesis is to examine if there is a difference in answers based on different characteristics of the interviewers and respondents.  The 15 most important questions from the survey were chosen in this thesis, and to further reduce the number of response variables principal component analysis was used. The new scores that were produced by the analysis were the reduced response variables, which kept the most important information from the questions in the survey. Thereafter multilevel analyses and regression analyses were performed to examine the effects.   The results showed that there was an effect of different characteristics in different questions in the survey. The characteristics that showed effect were the age of the interviewer, the length of the employment, the age of the respondent, education, sex and native language. Some of the questions also showed effect based on whether the respondent lived in a metropolitan region or not. / Norstat genomför en återkommande undersökning om hur mycket respondenten tittar på olika kanaler, hur olika media-apparater används, ägande av olika apparater, användning av kanalers sidor på internet och sociala medier samt internettjänster, tidningstjänster och streamingtjänster. Datamaterialet som ligger till grund för denna uppsats kommer från undersökningen när den ägde rum under hösten 2016. Syftet med uppsatsen är att hitta skillnader i enkätsvaren som uppstått av olika egenskaper hos intervjuaren och respondenten. De 15 viktigaste enkätfrågorna valdes och i denna uppsats har principalkomponentanalys används för att reducera antalet responsvariabler ännu mer. Det gjordes genom att skapa score som är ett färre antal responsvariabler vilka tillsammans förklarar de olika enkätfrågorna i undersökningen. Därefter har multilevelanalyser och regressionsanalyser använts för att analysera bakgrundsvariablernas påverkan på enkätfrågorna. Resultatet visade att det fanns effekt av olika egenskaper i olika sorters enkätfrågor. De egenskaper som visade effekt var intervjuarens ålder och anställningstid samt respondentens ålder, utbildning, kön och modersmål. Vissa frågor påverkades även av om respondenten bodde i en storstadsregion eller inte.

Accurate prediction of future events is of great interest in various contexts. This thesis focuses on forecasting and predicting energy usage in the industrial sector, which provides valuable information for government agencies to plan and allocate the available budget. More specifically, the purpose is to evaluate if using explanatory variables in a dynamic regression with seasonal autoregressive integrated moving average (SARIMA) errors improves the forecasting accuracy of quarterly energy usage in the industrial sector in Sweden compared to a standard SARIMA model. The SARIMA model used for comparison is SARIMA(1,0,0)(0,1,1), while the dynamic regression model used has the explanatory variable value added of the industrial sector and SARIMA(1,0,0)(0,1,1) errors. The forecast performance of the two models is compared for both quarterly and yearly forecast horizons using root mean squared error (RMSE) and mean absolute error (MAE). The results show that the RMSE and MAE of the dynamic regression model are lower for both forecast horizons compared to the SARIMA model. Also, a significance test (OOS-t) and an encompassing test (ENC-NEW) are employed, which show that the difference in forecasting accuracy is statistically significant and that the SARIMA forecast doesn’t encompass the forecast of the dynamic regression model.